ann based ph control report
TRANSCRIPT
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 136
1
A REPORT ON
ANN Based pH Control
Under the partial fulfilment of course
SUBMITTED BY
SUMIT GUPTA 2009A8PS290P
MALIK BULBUL SINGH 2009A8PS293P
RAGHAV SUBRAMANIAN 2009A8PS294P
SUBMITTED TO
Dr SUREKHA BHANOT
Professor
Department of Electronics and Instrumentation Engineering
BIRLA INSTITUTE OF TECHNOLOGY AND SCIENCE PILANI
04 APRIL 2012
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 236
2
ACKNOWLEDGEMENT
We sincerely thank Prof Surekha Bhanot Instructor in-charge INSTR C312 for
giving us this opportunity of gaining an experience in mathematical modeling
using MATLAB based Artificial Neural Networks We would also like to express
our deep sense of gratitude to Dr Surekha Bhanot for her valuable suggestions
and advice without which this report would not have been possible We are alsograteful to Mr Parikshit K Singh and Mr Rajesh Purohit tutorial instructors for
providing us with a clear understanding of the subject The vote of thanks will be
incomplete without the mention of seniors and our friends who have helped us in
making this project successful
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 336
3
TABLE OF CONTENTS
TOPIC Page No
Abstract 4
Introduction 5
Process Description and Modelling 8
Conclusion
References
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 436
4
ABSTRACT
This report aims at the modeling of pH neutralization process
which is a very important process in the chemical industry and
implementing servo control for the pH neutralization process in a CSTH
The dynamic behavior of neutralization process in (CSTR) was studied
and the process control was implemented using different control
strategies Neural Network (NARMA-L2 NN Predictive) control for
neutralization of weak acid with a strong acid (NaOH)
The report has been broadly divided into three parts where the
first part deals with the process modeling of the pH neutralization of a
weak acid with a strong base in CSTH and the derivation of the
mathematical model for the process The second part deals with ANN
(Artificial neural network) its evolution over the period of time its
basic understanding its various applications The third and the last
part deals with different control strategies that are available and have
been implemented till now for various process models specifically pH
neutralization process And the control methods that we have
implemented using Simulink and neural network toolbox which provide
NARMA-L2 NN Predictive controllers which can be trained as per the
model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 536
5
INTRODUCTION
The precise control of pH is vital in many processes Some of the
applications that require a precise control of pH are in the areas of wastewater treatment pharmaceuticals biotechnology and chemical
processing Wastewater treatment is especially difficult since it is
necessary for the effluent stream to remain neutral to prevent
corrosion to protect aquatic life or to provide neutral water for reuse
as process water or as boiler feed
In bioreactors the control of pH is important to support cell
growth In the production of pharmaceuticals a tight control of pH iscritical to maintain the quality of the products The control of pH has
long been recognized as a difficult problem The difficulties arise due to
frequent changes in the influent composition and the severe process
non-linearities The process non-linearity can be expressed as a S-
shaped static pH response (see Fig 1)
Several approaches have been suggested in the past to handle
non-linear aspects of pH control Some of these methods are genericmodel control internal model control reaction invariant control and
gain scheduled PI control The use of an adaptive control scheme may
at first seem to be the appropriate choice for the control of a pH
neutralization process as shown in many studies However satisfactory
long-term control behavior was not obtained for the continuous
running of an adaptive control scheme At times the use of adaptive
control scheme has resulted in a change of sign of the process model
such that the valve is driven to saturation
Due to this the adaptation is usually turned off when unusual pH
responses are observed Despite the many other advances in non-linear
control theory gain scheduled PI control remains the preferred choice
for the industries
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 636
6
In the standard gain scheduled control schemes the gains
selection for the PI controller is dependent on the current pH in the
continuous stirred tank reactor (CSTR) As the pH in the CSTR varies the
gain varies accordingly
In this report use of this control scheme has shown a vast
improvement on the performance of the control system Furthermorethis method does not demand significant computing resources and is
heuristically easy to understand and simple to implement These
characteristics should make this control scheme more appealing to be
put into industrial practice
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 736
7
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 836
8
PROCESS DESCRIPTION AND MODELLING
The practical system under study in this paper is a pH
neutralization system (see Fig 2) The pH is defined as a measure of
acidity or alkalinity of a solution containing water It is mathematically
defined for a dilute solution as the negative decimal logarithm of the
hydrogen ion concentration [H+] in the solution that is
pH = minuslog10[H+] helliphelliphellip (1)
Practical pH processes tends to be very complicated in terms of
variations of the species contained in the influent and the reagent and
the selection of the mixing equipment However there exist several
well known dynamical models accounting for the dominant
characteristics of a pH process in a CSTR The pH neutralization process
presented in this paper was adapted from the dynamical model
presented by McAvoy This model had been derived from first
principles and has been verified by experimental results
The model consists of two parts a dynamical model describing
the flow dynamics of concentrations of the influent compositions intothe CSTR followed by a static non-linearity characterizing the
physiochemical equilibrium conditions between these concentrations
Assuming that the pH neutralization process has two inlet streams the
first inlet stream contains an acid of concentration C 1 with a flow rate
of F 1 and the second inlet stream contains base with concentration C 2
and flow rate of F 2 The dynamic model for the CSTR is then given as
V = F 1C 1 minus (F 1 + F 2 ) ξ helliphelliphelliphelliphellip (2)
V
= F 2C 2 minus (F 1 + F 2 ) ζ helliphelliphelliphelliphelliphellip (3)
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 936
9
where the constant v is the volume of the content in the reactor
and ξ and ζ the concentrations of the acid and the base respectively
These equations describe how the concentrations vary dynamically
with time subject to the input streams F 1 and F 2 To obtain the pH in the
effluent stream a relation between instantaneous concentrations ξ and
ζ is needed
This relationship can be described as a non-linear algebraicequation known as the titration curve Depending on the chemical
species used the titration curve varies In this paper we consider the
case of a weak acid neutralized by a strong base Nominal process
operating conditions are provided in Table 1 Consider an acetic acid
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1036
10
(weak acid) denoted by HAC being neutralized by sodium hydroxide
(strong base) denoted by NaOH
The reactions are
H2OhArr H+
+ OHminus
HAC hArr H+
+ ACminus
NaOH rarr Na+
+ OHminus
The electro neutrality condition states that the sum of the charges of allions in the solution must be zero
this is given by
[Na+] + [H
+] = [OH
minus] + [AC
minus] helliphelliphelliphelliphellip (4)
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1136
11
where the symbol [middot] denotes the concentration of its argument
In water where the dissociation is incomplete we define the
dissociation constant of water as
K w = [H+][OH
minus] helliphelliphelliphelliphellip (5)
where K w = 10minus14
is the dissociation constant for water at 25C Similarly
we can define the dissociation of acetic acid as
K a = [ACminus][H
+] helliphelliphelliphelliphelliphellip (6)
[HAC]
where K a = 18 times 10minus5
is the dissociation constant of acetic acid at 25C
Defining the concentrations of ξ and ζ as
ξ = [HAC] + [ACminus] helliphelliphelliphelliphellip (7)
and
ζ = [Na+] helliphelliphelliphelliphelliphelliphellip (8)
we have a set of seven independent equations (Eqs (2) ndash (8)) with
seven unknowns which describes the dynamic behavior of this
neutralization process A more condensed form of the above equations
can be achieved by eliminating [OHminus] using Eq (5) [AC
minus] using Eq (4)
and [HAC] using Eq (6) The resulting are Eqs (2) (3) and (9)
[H+]
3+ (K a + ζ) [H
+]
2+ (K a(ζ minus ξ) minus K w ) [H
+] minus K wK a = 0 helliphelliphelliphelliphelliphellip (9)
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1236
12
A Simulink model was constructed using this derivation of the
dynamical model to represent the pH neutralization process between
acetic acid and sodium hydroxide (see Fig 3)
Fig 3 Mathematical Model Implementation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1336
13
Fig4 Model for pH Utilization
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1436
14
Fig5 Neutralization Curve simulated in Process Model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1536
15
Fig6 Final Control Model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1636
16
Fig 7 Model Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1736
17
Fig8 NN Predictive Controller
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1836
18
Fig9 Training Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1936
19
Fig10 Neural Network and Training Parameters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2036
20
Fig11 Validation Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2136
21
Fig12 Simulation using Randomly Varying Set point
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2236
22
ARTIFICIAL NEURAL NETWORK
Artificial neural networks are inspired by the early models of
sensory processing by the brain An artificial neural network can becreated by simulating a network of model neurons in a computer By
applying algorithms that mimic the processes of real neurons we can
make the network lsquolearnrsquo to solve many types of problems A model
neuron is referred to as a threshold unit and its function is illustrated in
Figure 1a It receives input from a number of other units or external
sources weighs each input and adds them up If the total input is above
a threshold the output of the unit is one otherwise it is zero
Therefore the output changes from 0 to 1
when the total weighted sum of inputs is equal to the threshold
Learning
If the classification problem is separable we still need a way
to set the weights and the threshold such that the threshold unit
correctly solves the classification problem This can be done in an
iterative manner by presenting examples with known classifications
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2336
23
one after another This process is called learning or training because it
resembles the process we go through when learning something
Simulation of learning by a computer involves making small changes in
the weights and the threshold each time a new example is presented insuch a way that the classification is improved The training can be
implemented by various different algorithms
Back-propagation
Training starts by setting all the weights in the network to small
random numbers Now for each input example the network gives an
output which starts randomly We measure the squared difference
between this output and the desired outputmdashthe correct class or value
The sum of all these numbers over all training examples is called the
total error of the network If this number was zero the network would
be perfect and the smaller the error the better the network
By choosing the weights that minimize the total error one can
obtain the neural network that best solves the problem at hand This isthe same as linear regression where the two parameters characterizing
the line are chosen such that the sum of squared differences between
the line and the data points is minimal In back-propagation the
weights and thresholds are changed each time an example is
presented such that the error gradually becomes smaller This is
repeated often hundreds of times until the error no longer changes
In back-propagation a numerical optimization technique called
gradient descent makes the math particularly simple the form of the
equations gave rise to the name of this method There are some
learning parameters (called learning rate and momentum) that need
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2436
24
tuning when using back-propagation and there are other problems to
consider For instance gradient descent is not guaranteed to find the
global minimum of the error so the result of the training depends on
the initial values of the weights
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2536
25
CONTROL SYSTEMS
Control systems are tightly intertwined in our daily lives so much
so that we take them for granted They may be as low tech andunglamorous as our flush toilet Or they may be as high tech as
electronic fuel injection in our cars that we now drive In fact there is
more than a handful of computer control systems in a typical car that
we now drive In everything from the engine to transmission shock
absorber brakes pollutant emission temperature and so forth there
is an embedded microprocessor controller keeping an eye out for us
The more gadgetry the more tiny controllers pulling the trick
behind our backs1 At the lower end of consumer electronic devices
we can bet on finding at least one embedded microcontroller In the
processing industry controllers play a crucial role in keeping our plants
running ndash virtually everything from simply filling up a storage tank to
complex separation processes and chemical reactors
To consider pH as a controlled variable we use a pH electrode to
measure its value and with a transmitter send the signal to a
controller which can be a little black box or a computer The controller
takes in the pH value and compares it with the desired pH what is
called the set point or the reference If the values are not the same
there is an error and the controller makes proper adjustments by
manipulating the acid or the base pump ndash the actuator
The adjustment is based on calculations made with a control
algorithm also called the control law The error is calculated at the
summing point where we take the desired pH minus the measured pH
Because of how we calculate the error this is a negative-feedback
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2636
26
mechanism When we change a specific operating condition meaning
the set point we would like for example the pH of the bioreactor to
follow our command
This is what we call servo control The pH value of the bioreactor
is subjected to external disturbances (also called load changes) and the
task of suppressing or rejecting the effects of disturbances is called
regulatory control Implementation of a controller may lead to
instability and the issue of system stability is a major concern The
control system also has to be robust such that it is not overly sensitive
to changes in process parameters
Neural Network in Control Systems
Neural networks have been applied successfully in the
identification and control of dynamic systems The universal
approximation capabilities of the multilayer perceptron make it a
popular choice for modeling nonlinear systems and for implementing
general-purpose nonlinear controllers This chapter introduces threepopular neural network architectures for prediction and control that
have been implemented in the Neural Network Toolbox software
Model Predictive Control
NARMA-L2 (or Feedback Linearization) Control
Model Reference Control
This chapter presents brief descriptions of each of these
architectures and demonstrates how you can use them There are
typically two steps involved when using neural networks for control
1 System identification
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2736
27
2 Control design
In the system identification stage you develop a neural network
model of the plant that you want to control In the control design stage
you use the neural network plant model to design (or train) the
controller In each of the three control architectures described in this
chapter the system identification stage is identical The control design
stage however is different for each architecture
bull For model predictive control the plant model is used to predict futur
behavior of the plant and an optimization algorithm is used to select th
control input that optimizes future performance
bull For NARMA-L2 control the controller is simply a rearrangement of th
plant model
bull For model reference control the controller is a neural network that
trained to control a plant so that it follows a reference model The neura
network plant model is used to assist in the controller training
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2836
28
Controllers in NNET Toolbox
Model Predictive Control mdash
This controller uses a neural network model to predict future plant
responses to potential control signals An optimization algorithm then
computes the control signals that optimize future plant performance
The neural network plant model is trained offline in batch form using
any of the training algorithms (This is true for all three control
architectures) The controller however requires a significant amount
of online computation because an optimization algorithm is performed
at each sample time to compute the optimal control input
NARMA-L2 Control mdash
This controller requires the least computation of these three
architectures The controller is simply a rearrangement of the neural
network plant model which is trained offline in batch form The only
online computation is a forward pass through the neural network
controller The drawback of this method is that the plant must either be
in companion form or be capable of approximation by a companion
form model
Model Reference Control mdash
The online computation of this controller like NARMA-L2 is
minimal However unlike NARMA-L2 the model reference architecturerequires that a separate neural network controller be trained offline in
addition to the neural network plant model The controller training is
computationally expensive because it requires the use of dynamic back
propagation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2936
29
Fig13 Neural Network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3036
30
Fig14 Testing Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 236
2
ACKNOWLEDGEMENT
We sincerely thank Prof Surekha Bhanot Instructor in-charge INSTR C312 for
giving us this opportunity of gaining an experience in mathematical modeling
using MATLAB based Artificial Neural Networks We would also like to express
our deep sense of gratitude to Dr Surekha Bhanot for her valuable suggestions
and advice without which this report would not have been possible We are alsograteful to Mr Parikshit K Singh and Mr Rajesh Purohit tutorial instructors for
providing us with a clear understanding of the subject The vote of thanks will be
incomplete without the mention of seniors and our friends who have helped us in
making this project successful
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 336
3
TABLE OF CONTENTS
TOPIC Page No
Abstract 4
Introduction 5
Process Description and Modelling 8
Conclusion
References
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 436
4
ABSTRACT
This report aims at the modeling of pH neutralization process
which is a very important process in the chemical industry and
implementing servo control for the pH neutralization process in a CSTH
The dynamic behavior of neutralization process in (CSTR) was studied
and the process control was implemented using different control
strategies Neural Network (NARMA-L2 NN Predictive) control for
neutralization of weak acid with a strong acid (NaOH)
The report has been broadly divided into three parts where the
first part deals with the process modeling of the pH neutralization of a
weak acid with a strong base in CSTH and the derivation of the
mathematical model for the process The second part deals with ANN
(Artificial neural network) its evolution over the period of time its
basic understanding its various applications The third and the last
part deals with different control strategies that are available and have
been implemented till now for various process models specifically pH
neutralization process And the control methods that we have
implemented using Simulink and neural network toolbox which provide
NARMA-L2 NN Predictive controllers which can be trained as per the
model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 536
5
INTRODUCTION
The precise control of pH is vital in many processes Some of the
applications that require a precise control of pH are in the areas of wastewater treatment pharmaceuticals biotechnology and chemical
processing Wastewater treatment is especially difficult since it is
necessary for the effluent stream to remain neutral to prevent
corrosion to protect aquatic life or to provide neutral water for reuse
as process water or as boiler feed
In bioreactors the control of pH is important to support cell
growth In the production of pharmaceuticals a tight control of pH iscritical to maintain the quality of the products The control of pH has
long been recognized as a difficult problem The difficulties arise due to
frequent changes in the influent composition and the severe process
non-linearities The process non-linearity can be expressed as a S-
shaped static pH response (see Fig 1)
Several approaches have been suggested in the past to handle
non-linear aspects of pH control Some of these methods are genericmodel control internal model control reaction invariant control and
gain scheduled PI control The use of an adaptive control scheme may
at first seem to be the appropriate choice for the control of a pH
neutralization process as shown in many studies However satisfactory
long-term control behavior was not obtained for the continuous
running of an adaptive control scheme At times the use of adaptive
control scheme has resulted in a change of sign of the process model
such that the valve is driven to saturation
Due to this the adaptation is usually turned off when unusual pH
responses are observed Despite the many other advances in non-linear
control theory gain scheduled PI control remains the preferred choice
for the industries
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 636
6
In the standard gain scheduled control schemes the gains
selection for the PI controller is dependent on the current pH in the
continuous stirred tank reactor (CSTR) As the pH in the CSTR varies the
gain varies accordingly
In this report use of this control scheme has shown a vast
improvement on the performance of the control system Furthermorethis method does not demand significant computing resources and is
heuristically easy to understand and simple to implement These
characteristics should make this control scheme more appealing to be
put into industrial practice
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 736
7
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 836
8
PROCESS DESCRIPTION AND MODELLING
The practical system under study in this paper is a pH
neutralization system (see Fig 2) The pH is defined as a measure of
acidity or alkalinity of a solution containing water It is mathematically
defined for a dilute solution as the negative decimal logarithm of the
hydrogen ion concentration [H+] in the solution that is
pH = minuslog10[H+] helliphelliphellip (1)
Practical pH processes tends to be very complicated in terms of
variations of the species contained in the influent and the reagent and
the selection of the mixing equipment However there exist several
well known dynamical models accounting for the dominant
characteristics of a pH process in a CSTR The pH neutralization process
presented in this paper was adapted from the dynamical model
presented by McAvoy This model had been derived from first
principles and has been verified by experimental results
The model consists of two parts a dynamical model describing
the flow dynamics of concentrations of the influent compositions intothe CSTR followed by a static non-linearity characterizing the
physiochemical equilibrium conditions between these concentrations
Assuming that the pH neutralization process has two inlet streams the
first inlet stream contains an acid of concentration C 1 with a flow rate
of F 1 and the second inlet stream contains base with concentration C 2
and flow rate of F 2 The dynamic model for the CSTR is then given as
V = F 1C 1 minus (F 1 + F 2 ) ξ helliphelliphelliphelliphellip (2)
V
= F 2C 2 minus (F 1 + F 2 ) ζ helliphelliphelliphelliphelliphellip (3)
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 936
9
where the constant v is the volume of the content in the reactor
and ξ and ζ the concentrations of the acid and the base respectively
These equations describe how the concentrations vary dynamically
with time subject to the input streams F 1 and F 2 To obtain the pH in the
effluent stream a relation between instantaneous concentrations ξ and
ζ is needed
This relationship can be described as a non-linear algebraicequation known as the titration curve Depending on the chemical
species used the titration curve varies In this paper we consider the
case of a weak acid neutralized by a strong base Nominal process
operating conditions are provided in Table 1 Consider an acetic acid
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1036
10
(weak acid) denoted by HAC being neutralized by sodium hydroxide
(strong base) denoted by NaOH
The reactions are
H2OhArr H+
+ OHminus
HAC hArr H+
+ ACminus
NaOH rarr Na+
+ OHminus
The electro neutrality condition states that the sum of the charges of allions in the solution must be zero
this is given by
[Na+] + [H
+] = [OH
minus] + [AC
minus] helliphelliphelliphelliphellip (4)
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1136
11
where the symbol [middot] denotes the concentration of its argument
In water where the dissociation is incomplete we define the
dissociation constant of water as
K w = [H+][OH
minus] helliphelliphelliphelliphellip (5)
where K w = 10minus14
is the dissociation constant for water at 25C Similarly
we can define the dissociation of acetic acid as
K a = [ACminus][H
+] helliphelliphelliphelliphelliphellip (6)
[HAC]
where K a = 18 times 10minus5
is the dissociation constant of acetic acid at 25C
Defining the concentrations of ξ and ζ as
ξ = [HAC] + [ACminus] helliphelliphelliphelliphellip (7)
and
ζ = [Na+] helliphelliphelliphelliphelliphelliphellip (8)
we have a set of seven independent equations (Eqs (2) ndash (8)) with
seven unknowns which describes the dynamic behavior of this
neutralization process A more condensed form of the above equations
can be achieved by eliminating [OHminus] using Eq (5) [AC
minus] using Eq (4)
and [HAC] using Eq (6) The resulting are Eqs (2) (3) and (9)
[H+]
3+ (K a + ζ) [H
+]
2+ (K a(ζ minus ξ) minus K w ) [H
+] minus K wK a = 0 helliphelliphelliphelliphelliphellip (9)
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1236
12
A Simulink model was constructed using this derivation of the
dynamical model to represent the pH neutralization process between
acetic acid and sodium hydroxide (see Fig 3)
Fig 3 Mathematical Model Implementation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1336
13
Fig4 Model for pH Utilization
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1436
14
Fig5 Neutralization Curve simulated in Process Model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1536
15
Fig6 Final Control Model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1636
16
Fig 7 Model Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1736
17
Fig8 NN Predictive Controller
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1836
18
Fig9 Training Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1936
19
Fig10 Neural Network and Training Parameters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2036
20
Fig11 Validation Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2136
21
Fig12 Simulation using Randomly Varying Set point
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2236
22
ARTIFICIAL NEURAL NETWORK
Artificial neural networks are inspired by the early models of
sensory processing by the brain An artificial neural network can becreated by simulating a network of model neurons in a computer By
applying algorithms that mimic the processes of real neurons we can
make the network lsquolearnrsquo to solve many types of problems A model
neuron is referred to as a threshold unit and its function is illustrated in
Figure 1a It receives input from a number of other units or external
sources weighs each input and adds them up If the total input is above
a threshold the output of the unit is one otherwise it is zero
Therefore the output changes from 0 to 1
when the total weighted sum of inputs is equal to the threshold
Learning
If the classification problem is separable we still need a way
to set the weights and the threshold such that the threshold unit
correctly solves the classification problem This can be done in an
iterative manner by presenting examples with known classifications
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2336
23
one after another This process is called learning or training because it
resembles the process we go through when learning something
Simulation of learning by a computer involves making small changes in
the weights and the threshold each time a new example is presented insuch a way that the classification is improved The training can be
implemented by various different algorithms
Back-propagation
Training starts by setting all the weights in the network to small
random numbers Now for each input example the network gives an
output which starts randomly We measure the squared difference
between this output and the desired outputmdashthe correct class or value
The sum of all these numbers over all training examples is called the
total error of the network If this number was zero the network would
be perfect and the smaller the error the better the network
By choosing the weights that minimize the total error one can
obtain the neural network that best solves the problem at hand This isthe same as linear regression where the two parameters characterizing
the line are chosen such that the sum of squared differences between
the line and the data points is minimal In back-propagation the
weights and thresholds are changed each time an example is
presented such that the error gradually becomes smaller This is
repeated often hundreds of times until the error no longer changes
In back-propagation a numerical optimization technique called
gradient descent makes the math particularly simple the form of the
equations gave rise to the name of this method There are some
learning parameters (called learning rate and momentum) that need
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2436
24
tuning when using back-propagation and there are other problems to
consider For instance gradient descent is not guaranteed to find the
global minimum of the error so the result of the training depends on
the initial values of the weights
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2536
25
CONTROL SYSTEMS
Control systems are tightly intertwined in our daily lives so much
so that we take them for granted They may be as low tech andunglamorous as our flush toilet Or they may be as high tech as
electronic fuel injection in our cars that we now drive In fact there is
more than a handful of computer control systems in a typical car that
we now drive In everything from the engine to transmission shock
absorber brakes pollutant emission temperature and so forth there
is an embedded microprocessor controller keeping an eye out for us
The more gadgetry the more tiny controllers pulling the trick
behind our backs1 At the lower end of consumer electronic devices
we can bet on finding at least one embedded microcontroller In the
processing industry controllers play a crucial role in keeping our plants
running ndash virtually everything from simply filling up a storage tank to
complex separation processes and chemical reactors
To consider pH as a controlled variable we use a pH electrode to
measure its value and with a transmitter send the signal to a
controller which can be a little black box or a computer The controller
takes in the pH value and compares it with the desired pH what is
called the set point or the reference If the values are not the same
there is an error and the controller makes proper adjustments by
manipulating the acid or the base pump ndash the actuator
The adjustment is based on calculations made with a control
algorithm also called the control law The error is calculated at the
summing point where we take the desired pH minus the measured pH
Because of how we calculate the error this is a negative-feedback
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2636
26
mechanism When we change a specific operating condition meaning
the set point we would like for example the pH of the bioreactor to
follow our command
This is what we call servo control The pH value of the bioreactor
is subjected to external disturbances (also called load changes) and the
task of suppressing or rejecting the effects of disturbances is called
regulatory control Implementation of a controller may lead to
instability and the issue of system stability is a major concern The
control system also has to be robust such that it is not overly sensitive
to changes in process parameters
Neural Network in Control Systems
Neural networks have been applied successfully in the
identification and control of dynamic systems The universal
approximation capabilities of the multilayer perceptron make it a
popular choice for modeling nonlinear systems and for implementing
general-purpose nonlinear controllers This chapter introduces threepopular neural network architectures for prediction and control that
have been implemented in the Neural Network Toolbox software
Model Predictive Control
NARMA-L2 (or Feedback Linearization) Control
Model Reference Control
This chapter presents brief descriptions of each of these
architectures and demonstrates how you can use them There are
typically two steps involved when using neural networks for control
1 System identification
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2736
27
2 Control design
In the system identification stage you develop a neural network
model of the plant that you want to control In the control design stage
you use the neural network plant model to design (or train) the
controller In each of the three control architectures described in this
chapter the system identification stage is identical The control design
stage however is different for each architecture
bull For model predictive control the plant model is used to predict futur
behavior of the plant and an optimization algorithm is used to select th
control input that optimizes future performance
bull For NARMA-L2 control the controller is simply a rearrangement of th
plant model
bull For model reference control the controller is a neural network that
trained to control a plant so that it follows a reference model The neura
network plant model is used to assist in the controller training
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2836
28
Controllers in NNET Toolbox
Model Predictive Control mdash
This controller uses a neural network model to predict future plant
responses to potential control signals An optimization algorithm then
computes the control signals that optimize future plant performance
The neural network plant model is trained offline in batch form using
any of the training algorithms (This is true for all three control
architectures) The controller however requires a significant amount
of online computation because an optimization algorithm is performed
at each sample time to compute the optimal control input
NARMA-L2 Control mdash
This controller requires the least computation of these three
architectures The controller is simply a rearrangement of the neural
network plant model which is trained offline in batch form The only
online computation is a forward pass through the neural network
controller The drawback of this method is that the plant must either be
in companion form or be capable of approximation by a companion
form model
Model Reference Control mdash
The online computation of this controller like NARMA-L2 is
minimal However unlike NARMA-L2 the model reference architecturerequires that a separate neural network controller be trained offline in
addition to the neural network plant model The controller training is
computationally expensive because it requires the use of dynamic back
propagation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2936
29
Fig13 Neural Network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3036
30
Fig14 Testing Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 336
3
TABLE OF CONTENTS
TOPIC Page No
Abstract 4
Introduction 5
Process Description and Modelling 8
Conclusion
References
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 436
4
ABSTRACT
This report aims at the modeling of pH neutralization process
which is a very important process in the chemical industry and
implementing servo control for the pH neutralization process in a CSTH
The dynamic behavior of neutralization process in (CSTR) was studied
and the process control was implemented using different control
strategies Neural Network (NARMA-L2 NN Predictive) control for
neutralization of weak acid with a strong acid (NaOH)
The report has been broadly divided into three parts where the
first part deals with the process modeling of the pH neutralization of a
weak acid with a strong base in CSTH and the derivation of the
mathematical model for the process The second part deals with ANN
(Artificial neural network) its evolution over the period of time its
basic understanding its various applications The third and the last
part deals with different control strategies that are available and have
been implemented till now for various process models specifically pH
neutralization process And the control methods that we have
implemented using Simulink and neural network toolbox which provide
NARMA-L2 NN Predictive controllers which can be trained as per the
model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 536
5
INTRODUCTION
The precise control of pH is vital in many processes Some of the
applications that require a precise control of pH are in the areas of wastewater treatment pharmaceuticals biotechnology and chemical
processing Wastewater treatment is especially difficult since it is
necessary for the effluent stream to remain neutral to prevent
corrosion to protect aquatic life or to provide neutral water for reuse
as process water or as boiler feed
In bioreactors the control of pH is important to support cell
growth In the production of pharmaceuticals a tight control of pH iscritical to maintain the quality of the products The control of pH has
long been recognized as a difficult problem The difficulties arise due to
frequent changes in the influent composition and the severe process
non-linearities The process non-linearity can be expressed as a S-
shaped static pH response (see Fig 1)
Several approaches have been suggested in the past to handle
non-linear aspects of pH control Some of these methods are genericmodel control internal model control reaction invariant control and
gain scheduled PI control The use of an adaptive control scheme may
at first seem to be the appropriate choice for the control of a pH
neutralization process as shown in many studies However satisfactory
long-term control behavior was not obtained for the continuous
running of an adaptive control scheme At times the use of adaptive
control scheme has resulted in a change of sign of the process model
such that the valve is driven to saturation
Due to this the adaptation is usually turned off when unusual pH
responses are observed Despite the many other advances in non-linear
control theory gain scheduled PI control remains the preferred choice
for the industries
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 636
6
In the standard gain scheduled control schemes the gains
selection for the PI controller is dependent on the current pH in the
continuous stirred tank reactor (CSTR) As the pH in the CSTR varies the
gain varies accordingly
In this report use of this control scheme has shown a vast
improvement on the performance of the control system Furthermorethis method does not demand significant computing resources and is
heuristically easy to understand and simple to implement These
characteristics should make this control scheme more appealing to be
put into industrial practice
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 736
7
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 836
8
PROCESS DESCRIPTION AND MODELLING
The practical system under study in this paper is a pH
neutralization system (see Fig 2) The pH is defined as a measure of
acidity or alkalinity of a solution containing water It is mathematically
defined for a dilute solution as the negative decimal logarithm of the
hydrogen ion concentration [H+] in the solution that is
pH = minuslog10[H+] helliphelliphellip (1)
Practical pH processes tends to be very complicated in terms of
variations of the species contained in the influent and the reagent and
the selection of the mixing equipment However there exist several
well known dynamical models accounting for the dominant
characteristics of a pH process in a CSTR The pH neutralization process
presented in this paper was adapted from the dynamical model
presented by McAvoy This model had been derived from first
principles and has been verified by experimental results
The model consists of two parts a dynamical model describing
the flow dynamics of concentrations of the influent compositions intothe CSTR followed by a static non-linearity characterizing the
physiochemical equilibrium conditions between these concentrations
Assuming that the pH neutralization process has two inlet streams the
first inlet stream contains an acid of concentration C 1 with a flow rate
of F 1 and the second inlet stream contains base with concentration C 2
and flow rate of F 2 The dynamic model for the CSTR is then given as
V = F 1C 1 minus (F 1 + F 2 ) ξ helliphelliphelliphelliphellip (2)
V
= F 2C 2 minus (F 1 + F 2 ) ζ helliphelliphelliphelliphelliphellip (3)
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 936
9
where the constant v is the volume of the content in the reactor
and ξ and ζ the concentrations of the acid and the base respectively
These equations describe how the concentrations vary dynamically
with time subject to the input streams F 1 and F 2 To obtain the pH in the
effluent stream a relation between instantaneous concentrations ξ and
ζ is needed
This relationship can be described as a non-linear algebraicequation known as the titration curve Depending on the chemical
species used the titration curve varies In this paper we consider the
case of a weak acid neutralized by a strong base Nominal process
operating conditions are provided in Table 1 Consider an acetic acid
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1036
10
(weak acid) denoted by HAC being neutralized by sodium hydroxide
(strong base) denoted by NaOH
The reactions are
H2OhArr H+
+ OHminus
HAC hArr H+
+ ACminus
NaOH rarr Na+
+ OHminus
The electro neutrality condition states that the sum of the charges of allions in the solution must be zero
this is given by
[Na+] + [H
+] = [OH
minus] + [AC
minus] helliphelliphelliphelliphellip (4)
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1136
11
where the symbol [middot] denotes the concentration of its argument
In water where the dissociation is incomplete we define the
dissociation constant of water as
K w = [H+][OH
minus] helliphelliphelliphelliphellip (5)
where K w = 10minus14
is the dissociation constant for water at 25C Similarly
we can define the dissociation of acetic acid as
K a = [ACminus][H
+] helliphelliphelliphelliphelliphellip (6)
[HAC]
where K a = 18 times 10minus5
is the dissociation constant of acetic acid at 25C
Defining the concentrations of ξ and ζ as
ξ = [HAC] + [ACminus] helliphelliphelliphelliphellip (7)
and
ζ = [Na+] helliphelliphelliphelliphelliphelliphellip (8)
we have a set of seven independent equations (Eqs (2) ndash (8)) with
seven unknowns which describes the dynamic behavior of this
neutralization process A more condensed form of the above equations
can be achieved by eliminating [OHminus] using Eq (5) [AC
minus] using Eq (4)
and [HAC] using Eq (6) The resulting are Eqs (2) (3) and (9)
[H+]
3+ (K a + ζ) [H
+]
2+ (K a(ζ minus ξ) minus K w ) [H
+] minus K wK a = 0 helliphelliphelliphelliphelliphellip (9)
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1236
12
A Simulink model was constructed using this derivation of the
dynamical model to represent the pH neutralization process between
acetic acid and sodium hydroxide (see Fig 3)
Fig 3 Mathematical Model Implementation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1336
13
Fig4 Model for pH Utilization
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1436
14
Fig5 Neutralization Curve simulated in Process Model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1536
15
Fig6 Final Control Model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1636
16
Fig 7 Model Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1736
17
Fig8 NN Predictive Controller
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1836
18
Fig9 Training Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1936
19
Fig10 Neural Network and Training Parameters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2036
20
Fig11 Validation Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2136
21
Fig12 Simulation using Randomly Varying Set point
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2236
22
ARTIFICIAL NEURAL NETWORK
Artificial neural networks are inspired by the early models of
sensory processing by the brain An artificial neural network can becreated by simulating a network of model neurons in a computer By
applying algorithms that mimic the processes of real neurons we can
make the network lsquolearnrsquo to solve many types of problems A model
neuron is referred to as a threshold unit and its function is illustrated in
Figure 1a It receives input from a number of other units or external
sources weighs each input and adds them up If the total input is above
a threshold the output of the unit is one otherwise it is zero
Therefore the output changes from 0 to 1
when the total weighted sum of inputs is equal to the threshold
Learning
If the classification problem is separable we still need a way
to set the weights and the threshold such that the threshold unit
correctly solves the classification problem This can be done in an
iterative manner by presenting examples with known classifications
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2336
23
one after another This process is called learning or training because it
resembles the process we go through when learning something
Simulation of learning by a computer involves making small changes in
the weights and the threshold each time a new example is presented insuch a way that the classification is improved The training can be
implemented by various different algorithms
Back-propagation
Training starts by setting all the weights in the network to small
random numbers Now for each input example the network gives an
output which starts randomly We measure the squared difference
between this output and the desired outputmdashthe correct class or value
The sum of all these numbers over all training examples is called the
total error of the network If this number was zero the network would
be perfect and the smaller the error the better the network
By choosing the weights that minimize the total error one can
obtain the neural network that best solves the problem at hand This isthe same as linear regression where the two parameters characterizing
the line are chosen such that the sum of squared differences between
the line and the data points is minimal In back-propagation the
weights and thresholds are changed each time an example is
presented such that the error gradually becomes smaller This is
repeated often hundreds of times until the error no longer changes
In back-propagation a numerical optimization technique called
gradient descent makes the math particularly simple the form of the
equations gave rise to the name of this method There are some
learning parameters (called learning rate and momentum) that need
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2436
24
tuning when using back-propagation and there are other problems to
consider For instance gradient descent is not guaranteed to find the
global minimum of the error so the result of the training depends on
the initial values of the weights
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2536
25
CONTROL SYSTEMS
Control systems are tightly intertwined in our daily lives so much
so that we take them for granted They may be as low tech andunglamorous as our flush toilet Or they may be as high tech as
electronic fuel injection in our cars that we now drive In fact there is
more than a handful of computer control systems in a typical car that
we now drive In everything from the engine to transmission shock
absorber brakes pollutant emission temperature and so forth there
is an embedded microprocessor controller keeping an eye out for us
The more gadgetry the more tiny controllers pulling the trick
behind our backs1 At the lower end of consumer electronic devices
we can bet on finding at least one embedded microcontroller In the
processing industry controllers play a crucial role in keeping our plants
running ndash virtually everything from simply filling up a storage tank to
complex separation processes and chemical reactors
To consider pH as a controlled variable we use a pH electrode to
measure its value and with a transmitter send the signal to a
controller which can be a little black box or a computer The controller
takes in the pH value and compares it with the desired pH what is
called the set point or the reference If the values are not the same
there is an error and the controller makes proper adjustments by
manipulating the acid or the base pump ndash the actuator
The adjustment is based on calculations made with a control
algorithm also called the control law The error is calculated at the
summing point where we take the desired pH minus the measured pH
Because of how we calculate the error this is a negative-feedback
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2636
26
mechanism When we change a specific operating condition meaning
the set point we would like for example the pH of the bioreactor to
follow our command
This is what we call servo control The pH value of the bioreactor
is subjected to external disturbances (also called load changes) and the
task of suppressing or rejecting the effects of disturbances is called
regulatory control Implementation of a controller may lead to
instability and the issue of system stability is a major concern The
control system also has to be robust such that it is not overly sensitive
to changes in process parameters
Neural Network in Control Systems
Neural networks have been applied successfully in the
identification and control of dynamic systems The universal
approximation capabilities of the multilayer perceptron make it a
popular choice for modeling nonlinear systems and for implementing
general-purpose nonlinear controllers This chapter introduces threepopular neural network architectures for prediction and control that
have been implemented in the Neural Network Toolbox software
Model Predictive Control
NARMA-L2 (or Feedback Linearization) Control
Model Reference Control
This chapter presents brief descriptions of each of these
architectures and demonstrates how you can use them There are
typically two steps involved when using neural networks for control
1 System identification
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2736
27
2 Control design
In the system identification stage you develop a neural network
model of the plant that you want to control In the control design stage
you use the neural network plant model to design (or train) the
controller In each of the three control architectures described in this
chapter the system identification stage is identical The control design
stage however is different for each architecture
bull For model predictive control the plant model is used to predict futur
behavior of the plant and an optimization algorithm is used to select th
control input that optimizes future performance
bull For NARMA-L2 control the controller is simply a rearrangement of th
plant model
bull For model reference control the controller is a neural network that
trained to control a plant so that it follows a reference model The neura
network plant model is used to assist in the controller training
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2836
28
Controllers in NNET Toolbox
Model Predictive Control mdash
This controller uses a neural network model to predict future plant
responses to potential control signals An optimization algorithm then
computes the control signals that optimize future plant performance
The neural network plant model is trained offline in batch form using
any of the training algorithms (This is true for all three control
architectures) The controller however requires a significant amount
of online computation because an optimization algorithm is performed
at each sample time to compute the optimal control input
NARMA-L2 Control mdash
This controller requires the least computation of these three
architectures The controller is simply a rearrangement of the neural
network plant model which is trained offline in batch form The only
online computation is a forward pass through the neural network
controller The drawback of this method is that the plant must either be
in companion form or be capable of approximation by a companion
form model
Model Reference Control mdash
The online computation of this controller like NARMA-L2 is
minimal However unlike NARMA-L2 the model reference architecturerequires that a separate neural network controller be trained offline in
addition to the neural network plant model The controller training is
computationally expensive because it requires the use of dynamic back
propagation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2936
29
Fig13 Neural Network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3036
30
Fig14 Testing Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 436
4
ABSTRACT
This report aims at the modeling of pH neutralization process
which is a very important process in the chemical industry and
implementing servo control for the pH neutralization process in a CSTH
The dynamic behavior of neutralization process in (CSTR) was studied
and the process control was implemented using different control
strategies Neural Network (NARMA-L2 NN Predictive) control for
neutralization of weak acid with a strong acid (NaOH)
The report has been broadly divided into three parts where the
first part deals with the process modeling of the pH neutralization of a
weak acid with a strong base in CSTH and the derivation of the
mathematical model for the process The second part deals with ANN
(Artificial neural network) its evolution over the period of time its
basic understanding its various applications The third and the last
part deals with different control strategies that are available and have
been implemented till now for various process models specifically pH
neutralization process And the control methods that we have
implemented using Simulink and neural network toolbox which provide
NARMA-L2 NN Predictive controllers which can be trained as per the
model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 536
5
INTRODUCTION
The precise control of pH is vital in many processes Some of the
applications that require a precise control of pH are in the areas of wastewater treatment pharmaceuticals biotechnology and chemical
processing Wastewater treatment is especially difficult since it is
necessary for the effluent stream to remain neutral to prevent
corrosion to protect aquatic life or to provide neutral water for reuse
as process water or as boiler feed
In bioreactors the control of pH is important to support cell
growth In the production of pharmaceuticals a tight control of pH iscritical to maintain the quality of the products The control of pH has
long been recognized as a difficult problem The difficulties arise due to
frequent changes in the influent composition and the severe process
non-linearities The process non-linearity can be expressed as a S-
shaped static pH response (see Fig 1)
Several approaches have been suggested in the past to handle
non-linear aspects of pH control Some of these methods are genericmodel control internal model control reaction invariant control and
gain scheduled PI control The use of an adaptive control scheme may
at first seem to be the appropriate choice for the control of a pH
neutralization process as shown in many studies However satisfactory
long-term control behavior was not obtained for the continuous
running of an adaptive control scheme At times the use of adaptive
control scheme has resulted in a change of sign of the process model
such that the valve is driven to saturation
Due to this the adaptation is usually turned off when unusual pH
responses are observed Despite the many other advances in non-linear
control theory gain scheduled PI control remains the preferred choice
for the industries
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 636
6
In the standard gain scheduled control schemes the gains
selection for the PI controller is dependent on the current pH in the
continuous stirred tank reactor (CSTR) As the pH in the CSTR varies the
gain varies accordingly
In this report use of this control scheme has shown a vast
improvement on the performance of the control system Furthermorethis method does not demand significant computing resources and is
heuristically easy to understand and simple to implement These
characteristics should make this control scheme more appealing to be
put into industrial practice
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 736
7
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 836
8
PROCESS DESCRIPTION AND MODELLING
The practical system under study in this paper is a pH
neutralization system (see Fig 2) The pH is defined as a measure of
acidity or alkalinity of a solution containing water It is mathematically
defined for a dilute solution as the negative decimal logarithm of the
hydrogen ion concentration [H+] in the solution that is
pH = minuslog10[H+] helliphelliphellip (1)
Practical pH processes tends to be very complicated in terms of
variations of the species contained in the influent and the reagent and
the selection of the mixing equipment However there exist several
well known dynamical models accounting for the dominant
characteristics of a pH process in a CSTR The pH neutralization process
presented in this paper was adapted from the dynamical model
presented by McAvoy This model had been derived from first
principles and has been verified by experimental results
The model consists of two parts a dynamical model describing
the flow dynamics of concentrations of the influent compositions intothe CSTR followed by a static non-linearity characterizing the
physiochemical equilibrium conditions between these concentrations
Assuming that the pH neutralization process has two inlet streams the
first inlet stream contains an acid of concentration C 1 with a flow rate
of F 1 and the second inlet stream contains base with concentration C 2
and flow rate of F 2 The dynamic model for the CSTR is then given as
V = F 1C 1 minus (F 1 + F 2 ) ξ helliphelliphelliphelliphellip (2)
V
= F 2C 2 minus (F 1 + F 2 ) ζ helliphelliphelliphelliphelliphellip (3)
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 936
9
where the constant v is the volume of the content in the reactor
and ξ and ζ the concentrations of the acid and the base respectively
These equations describe how the concentrations vary dynamically
with time subject to the input streams F 1 and F 2 To obtain the pH in the
effluent stream a relation between instantaneous concentrations ξ and
ζ is needed
This relationship can be described as a non-linear algebraicequation known as the titration curve Depending on the chemical
species used the titration curve varies In this paper we consider the
case of a weak acid neutralized by a strong base Nominal process
operating conditions are provided in Table 1 Consider an acetic acid
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1036
10
(weak acid) denoted by HAC being neutralized by sodium hydroxide
(strong base) denoted by NaOH
The reactions are
H2OhArr H+
+ OHminus
HAC hArr H+
+ ACminus
NaOH rarr Na+
+ OHminus
The electro neutrality condition states that the sum of the charges of allions in the solution must be zero
this is given by
[Na+] + [H
+] = [OH
minus] + [AC
minus] helliphelliphelliphelliphellip (4)
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1136
11
where the symbol [middot] denotes the concentration of its argument
In water where the dissociation is incomplete we define the
dissociation constant of water as
K w = [H+][OH
minus] helliphelliphelliphelliphellip (5)
where K w = 10minus14
is the dissociation constant for water at 25C Similarly
we can define the dissociation of acetic acid as
K a = [ACminus][H
+] helliphelliphelliphelliphelliphellip (6)
[HAC]
where K a = 18 times 10minus5
is the dissociation constant of acetic acid at 25C
Defining the concentrations of ξ and ζ as
ξ = [HAC] + [ACminus] helliphelliphelliphelliphellip (7)
and
ζ = [Na+] helliphelliphelliphelliphelliphelliphellip (8)
we have a set of seven independent equations (Eqs (2) ndash (8)) with
seven unknowns which describes the dynamic behavior of this
neutralization process A more condensed form of the above equations
can be achieved by eliminating [OHminus] using Eq (5) [AC
minus] using Eq (4)
and [HAC] using Eq (6) The resulting are Eqs (2) (3) and (9)
[H+]
3+ (K a + ζ) [H
+]
2+ (K a(ζ minus ξ) minus K w ) [H
+] minus K wK a = 0 helliphelliphelliphelliphelliphellip (9)
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1236
12
A Simulink model was constructed using this derivation of the
dynamical model to represent the pH neutralization process between
acetic acid and sodium hydroxide (see Fig 3)
Fig 3 Mathematical Model Implementation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1336
13
Fig4 Model for pH Utilization
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1436
14
Fig5 Neutralization Curve simulated in Process Model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1536
15
Fig6 Final Control Model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1636
16
Fig 7 Model Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1736
17
Fig8 NN Predictive Controller
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1836
18
Fig9 Training Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1936
19
Fig10 Neural Network and Training Parameters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2036
20
Fig11 Validation Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2136
21
Fig12 Simulation using Randomly Varying Set point
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2236
22
ARTIFICIAL NEURAL NETWORK
Artificial neural networks are inspired by the early models of
sensory processing by the brain An artificial neural network can becreated by simulating a network of model neurons in a computer By
applying algorithms that mimic the processes of real neurons we can
make the network lsquolearnrsquo to solve many types of problems A model
neuron is referred to as a threshold unit and its function is illustrated in
Figure 1a It receives input from a number of other units or external
sources weighs each input and adds them up If the total input is above
a threshold the output of the unit is one otherwise it is zero
Therefore the output changes from 0 to 1
when the total weighted sum of inputs is equal to the threshold
Learning
If the classification problem is separable we still need a way
to set the weights and the threshold such that the threshold unit
correctly solves the classification problem This can be done in an
iterative manner by presenting examples with known classifications
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2336
23
one after another This process is called learning or training because it
resembles the process we go through when learning something
Simulation of learning by a computer involves making small changes in
the weights and the threshold each time a new example is presented insuch a way that the classification is improved The training can be
implemented by various different algorithms
Back-propagation
Training starts by setting all the weights in the network to small
random numbers Now for each input example the network gives an
output which starts randomly We measure the squared difference
between this output and the desired outputmdashthe correct class or value
The sum of all these numbers over all training examples is called the
total error of the network If this number was zero the network would
be perfect and the smaller the error the better the network
By choosing the weights that minimize the total error one can
obtain the neural network that best solves the problem at hand This isthe same as linear regression where the two parameters characterizing
the line are chosen such that the sum of squared differences between
the line and the data points is minimal In back-propagation the
weights and thresholds are changed each time an example is
presented such that the error gradually becomes smaller This is
repeated often hundreds of times until the error no longer changes
In back-propagation a numerical optimization technique called
gradient descent makes the math particularly simple the form of the
equations gave rise to the name of this method There are some
learning parameters (called learning rate and momentum) that need
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2436
24
tuning when using back-propagation and there are other problems to
consider For instance gradient descent is not guaranteed to find the
global minimum of the error so the result of the training depends on
the initial values of the weights
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2536
25
CONTROL SYSTEMS
Control systems are tightly intertwined in our daily lives so much
so that we take them for granted They may be as low tech andunglamorous as our flush toilet Or they may be as high tech as
electronic fuel injection in our cars that we now drive In fact there is
more than a handful of computer control systems in a typical car that
we now drive In everything from the engine to transmission shock
absorber brakes pollutant emission temperature and so forth there
is an embedded microprocessor controller keeping an eye out for us
The more gadgetry the more tiny controllers pulling the trick
behind our backs1 At the lower end of consumer electronic devices
we can bet on finding at least one embedded microcontroller In the
processing industry controllers play a crucial role in keeping our plants
running ndash virtually everything from simply filling up a storage tank to
complex separation processes and chemical reactors
To consider pH as a controlled variable we use a pH electrode to
measure its value and with a transmitter send the signal to a
controller which can be a little black box or a computer The controller
takes in the pH value and compares it with the desired pH what is
called the set point or the reference If the values are not the same
there is an error and the controller makes proper adjustments by
manipulating the acid or the base pump ndash the actuator
The adjustment is based on calculations made with a control
algorithm also called the control law The error is calculated at the
summing point where we take the desired pH minus the measured pH
Because of how we calculate the error this is a negative-feedback
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2636
26
mechanism When we change a specific operating condition meaning
the set point we would like for example the pH of the bioreactor to
follow our command
This is what we call servo control The pH value of the bioreactor
is subjected to external disturbances (also called load changes) and the
task of suppressing or rejecting the effects of disturbances is called
regulatory control Implementation of a controller may lead to
instability and the issue of system stability is a major concern The
control system also has to be robust such that it is not overly sensitive
to changes in process parameters
Neural Network in Control Systems
Neural networks have been applied successfully in the
identification and control of dynamic systems The universal
approximation capabilities of the multilayer perceptron make it a
popular choice for modeling nonlinear systems and for implementing
general-purpose nonlinear controllers This chapter introduces threepopular neural network architectures for prediction and control that
have been implemented in the Neural Network Toolbox software
Model Predictive Control
NARMA-L2 (or Feedback Linearization) Control
Model Reference Control
This chapter presents brief descriptions of each of these
architectures and demonstrates how you can use them There are
typically two steps involved when using neural networks for control
1 System identification
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2736
27
2 Control design
In the system identification stage you develop a neural network
model of the plant that you want to control In the control design stage
you use the neural network plant model to design (or train) the
controller In each of the three control architectures described in this
chapter the system identification stage is identical The control design
stage however is different for each architecture
bull For model predictive control the plant model is used to predict futur
behavior of the plant and an optimization algorithm is used to select th
control input that optimizes future performance
bull For NARMA-L2 control the controller is simply a rearrangement of th
plant model
bull For model reference control the controller is a neural network that
trained to control a plant so that it follows a reference model The neura
network plant model is used to assist in the controller training
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2836
28
Controllers in NNET Toolbox
Model Predictive Control mdash
This controller uses a neural network model to predict future plant
responses to potential control signals An optimization algorithm then
computes the control signals that optimize future plant performance
The neural network plant model is trained offline in batch form using
any of the training algorithms (This is true for all three control
architectures) The controller however requires a significant amount
of online computation because an optimization algorithm is performed
at each sample time to compute the optimal control input
NARMA-L2 Control mdash
This controller requires the least computation of these three
architectures The controller is simply a rearrangement of the neural
network plant model which is trained offline in batch form The only
online computation is a forward pass through the neural network
controller The drawback of this method is that the plant must either be
in companion form or be capable of approximation by a companion
form model
Model Reference Control mdash
The online computation of this controller like NARMA-L2 is
minimal However unlike NARMA-L2 the model reference architecturerequires that a separate neural network controller be trained offline in
addition to the neural network plant model The controller training is
computationally expensive because it requires the use of dynamic back
propagation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2936
29
Fig13 Neural Network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3036
30
Fig14 Testing Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 536
5
INTRODUCTION
The precise control of pH is vital in many processes Some of the
applications that require a precise control of pH are in the areas of wastewater treatment pharmaceuticals biotechnology and chemical
processing Wastewater treatment is especially difficult since it is
necessary for the effluent stream to remain neutral to prevent
corrosion to protect aquatic life or to provide neutral water for reuse
as process water or as boiler feed
In bioreactors the control of pH is important to support cell
growth In the production of pharmaceuticals a tight control of pH iscritical to maintain the quality of the products The control of pH has
long been recognized as a difficult problem The difficulties arise due to
frequent changes in the influent composition and the severe process
non-linearities The process non-linearity can be expressed as a S-
shaped static pH response (see Fig 1)
Several approaches have been suggested in the past to handle
non-linear aspects of pH control Some of these methods are genericmodel control internal model control reaction invariant control and
gain scheduled PI control The use of an adaptive control scheme may
at first seem to be the appropriate choice for the control of a pH
neutralization process as shown in many studies However satisfactory
long-term control behavior was not obtained for the continuous
running of an adaptive control scheme At times the use of adaptive
control scheme has resulted in a change of sign of the process model
such that the valve is driven to saturation
Due to this the adaptation is usually turned off when unusual pH
responses are observed Despite the many other advances in non-linear
control theory gain scheduled PI control remains the preferred choice
for the industries
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 636
6
In the standard gain scheduled control schemes the gains
selection for the PI controller is dependent on the current pH in the
continuous stirred tank reactor (CSTR) As the pH in the CSTR varies the
gain varies accordingly
In this report use of this control scheme has shown a vast
improvement on the performance of the control system Furthermorethis method does not demand significant computing resources and is
heuristically easy to understand and simple to implement These
characteristics should make this control scheme more appealing to be
put into industrial practice
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 736
7
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 836
8
PROCESS DESCRIPTION AND MODELLING
The practical system under study in this paper is a pH
neutralization system (see Fig 2) The pH is defined as a measure of
acidity or alkalinity of a solution containing water It is mathematically
defined for a dilute solution as the negative decimal logarithm of the
hydrogen ion concentration [H+] in the solution that is
pH = minuslog10[H+] helliphelliphellip (1)
Practical pH processes tends to be very complicated in terms of
variations of the species contained in the influent and the reagent and
the selection of the mixing equipment However there exist several
well known dynamical models accounting for the dominant
characteristics of a pH process in a CSTR The pH neutralization process
presented in this paper was adapted from the dynamical model
presented by McAvoy This model had been derived from first
principles and has been verified by experimental results
The model consists of two parts a dynamical model describing
the flow dynamics of concentrations of the influent compositions intothe CSTR followed by a static non-linearity characterizing the
physiochemical equilibrium conditions between these concentrations
Assuming that the pH neutralization process has two inlet streams the
first inlet stream contains an acid of concentration C 1 with a flow rate
of F 1 and the second inlet stream contains base with concentration C 2
and flow rate of F 2 The dynamic model for the CSTR is then given as
V = F 1C 1 minus (F 1 + F 2 ) ξ helliphelliphelliphelliphellip (2)
V
= F 2C 2 minus (F 1 + F 2 ) ζ helliphelliphelliphelliphelliphellip (3)
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 936
9
where the constant v is the volume of the content in the reactor
and ξ and ζ the concentrations of the acid and the base respectively
These equations describe how the concentrations vary dynamically
with time subject to the input streams F 1 and F 2 To obtain the pH in the
effluent stream a relation between instantaneous concentrations ξ and
ζ is needed
This relationship can be described as a non-linear algebraicequation known as the titration curve Depending on the chemical
species used the titration curve varies In this paper we consider the
case of a weak acid neutralized by a strong base Nominal process
operating conditions are provided in Table 1 Consider an acetic acid
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1036
10
(weak acid) denoted by HAC being neutralized by sodium hydroxide
(strong base) denoted by NaOH
The reactions are
H2OhArr H+
+ OHminus
HAC hArr H+
+ ACminus
NaOH rarr Na+
+ OHminus
The electro neutrality condition states that the sum of the charges of allions in the solution must be zero
this is given by
[Na+] + [H
+] = [OH
minus] + [AC
minus] helliphelliphelliphelliphellip (4)
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1136
11
where the symbol [middot] denotes the concentration of its argument
In water where the dissociation is incomplete we define the
dissociation constant of water as
K w = [H+][OH
minus] helliphelliphelliphelliphellip (5)
where K w = 10minus14
is the dissociation constant for water at 25C Similarly
we can define the dissociation of acetic acid as
K a = [ACminus][H
+] helliphelliphelliphelliphelliphellip (6)
[HAC]
where K a = 18 times 10minus5
is the dissociation constant of acetic acid at 25C
Defining the concentrations of ξ and ζ as
ξ = [HAC] + [ACminus] helliphelliphelliphelliphellip (7)
and
ζ = [Na+] helliphelliphelliphelliphelliphelliphellip (8)
we have a set of seven independent equations (Eqs (2) ndash (8)) with
seven unknowns which describes the dynamic behavior of this
neutralization process A more condensed form of the above equations
can be achieved by eliminating [OHminus] using Eq (5) [AC
minus] using Eq (4)
and [HAC] using Eq (6) The resulting are Eqs (2) (3) and (9)
[H+]
3+ (K a + ζ) [H
+]
2+ (K a(ζ minus ξ) minus K w ) [H
+] minus K wK a = 0 helliphelliphelliphelliphelliphellip (9)
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1236
12
A Simulink model was constructed using this derivation of the
dynamical model to represent the pH neutralization process between
acetic acid and sodium hydroxide (see Fig 3)
Fig 3 Mathematical Model Implementation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1336
13
Fig4 Model for pH Utilization
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1436
14
Fig5 Neutralization Curve simulated in Process Model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1536
15
Fig6 Final Control Model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1636
16
Fig 7 Model Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1736
17
Fig8 NN Predictive Controller
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1836
18
Fig9 Training Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1936
19
Fig10 Neural Network and Training Parameters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2036
20
Fig11 Validation Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2136
21
Fig12 Simulation using Randomly Varying Set point
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2236
22
ARTIFICIAL NEURAL NETWORK
Artificial neural networks are inspired by the early models of
sensory processing by the brain An artificial neural network can becreated by simulating a network of model neurons in a computer By
applying algorithms that mimic the processes of real neurons we can
make the network lsquolearnrsquo to solve many types of problems A model
neuron is referred to as a threshold unit and its function is illustrated in
Figure 1a It receives input from a number of other units or external
sources weighs each input and adds them up If the total input is above
a threshold the output of the unit is one otherwise it is zero
Therefore the output changes from 0 to 1
when the total weighted sum of inputs is equal to the threshold
Learning
If the classification problem is separable we still need a way
to set the weights and the threshold such that the threshold unit
correctly solves the classification problem This can be done in an
iterative manner by presenting examples with known classifications
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2336
23
one after another This process is called learning or training because it
resembles the process we go through when learning something
Simulation of learning by a computer involves making small changes in
the weights and the threshold each time a new example is presented insuch a way that the classification is improved The training can be
implemented by various different algorithms
Back-propagation
Training starts by setting all the weights in the network to small
random numbers Now for each input example the network gives an
output which starts randomly We measure the squared difference
between this output and the desired outputmdashthe correct class or value
The sum of all these numbers over all training examples is called the
total error of the network If this number was zero the network would
be perfect and the smaller the error the better the network
By choosing the weights that minimize the total error one can
obtain the neural network that best solves the problem at hand This isthe same as linear regression where the two parameters characterizing
the line are chosen such that the sum of squared differences between
the line and the data points is minimal In back-propagation the
weights and thresholds are changed each time an example is
presented such that the error gradually becomes smaller This is
repeated often hundreds of times until the error no longer changes
In back-propagation a numerical optimization technique called
gradient descent makes the math particularly simple the form of the
equations gave rise to the name of this method There are some
learning parameters (called learning rate and momentum) that need
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2436
24
tuning when using back-propagation and there are other problems to
consider For instance gradient descent is not guaranteed to find the
global minimum of the error so the result of the training depends on
the initial values of the weights
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2536
25
CONTROL SYSTEMS
Control systems are tightly intertwined in our daily lives so much
so that we take them for granted They may be as low tech andunglamorous as our flush toilet Or they may be as high tech as
electronic fuel injection in our cars that we now drive In fact there is
more than a handful of computer control systems in a typical car that
we now drive In everything from the engine to transmission shock
absorber brakes pollutant emission temperature and so forth there
is an embedded microprocessor controller keeping an eye out for us
The more gadgetry the more tiny controllers pulling the trick
behind our backs1 At the lower end of consumer electronic devices
we can bet on finding at least one embedded microcontroller In the
processing industry controllers play a crucial role in keeping our plants
running ndash virtually everything from simply filling up a storage tank to
complex separation processes and chemical reactors
To consider pH as a controlled variable we use a pH electrode to
measure its value and with a transmitter send the signal to a
controller which can be a little black box or a computer The controller
takes in the pH value and compares it with the desired pH what is
called the set point or the reference If the values are not the same
there is an error and the controller makes proper adjustments by
manipulating the acid or the base pump ndash the actuator
The adjustment is based on calculations made with a control
algorithm also called the control law The error is calculated at the
summing point where we take the desired pH minus the measured pH
Because of how we calculate the error this is a negative-feedback
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2636
26
mechanism When we change a specific operating condition meaning
the set point we would like for example the pH of the bioreactor to
follow our command
This is what we call servo control The pH value of the bioreactor
is subjected to external disturbances (also called load changes) and the
task of suppressing or rejecting the effects of disturbances is called
regulatory control Implementation of a controller may lead to
instability and the issue of system stability is a major concern The
control system also has to be robust such that it is not overly sensitive
to changes in process parameters
Neural Network in Control Systems
Neural networks have been applied successfully in the
identification and control of dynamic systems The universal
approximation capabilities of the multilayer perceptron make it a
popular choice for modeling nonlinear systems and for implementing
general-purpose nonlinear controllers This chapter introduces threepopular neural network architectures for prediction and control that
have been implemented in the Neural Network Toolbox software
Model Predictive Control
NARMA-L2 (or Feedback Linearization) Control
Model Reference Control
This chapter presents brief descriptions of each of these
architectures and demonstrates how you can use them There are
typically two steps involved when using neural networks for control
1 System identification
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2736
27
2 Control design
In the system identification stage you develop a neural network
model of the plant that you want to control In the control design stage
you use the neural network plant model to design (or train) the
controller In each of the three control architectures described in this
chapter the system identification stage is identical The control design
stage however is different for each architecture
bull For model predictive control the plant model is used to predict futur
behavior of the plant and an optimization algorithm is used to select th
control input that optimizes future performance
bull For NARMA-L2 control the controller is simply a rearrangement of th
plant model
bull For model reference control the controller is a neural network that
trained to control a plant so that it follows a reference model The neura
network plant model is used to assist in the controller training
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2836
28
Controllers in NNET Toolbox
Model Predictive Control mdash
This controller uses a neural network model to predict future plant
responses to potential control signals An optimization algorithm then
computes the control signals that optimize future plant performance
The neural network plant model is trained offline in batch form using
any of the training algorithms (This is true for all three control
architectures) The controller however requires a significant amount
of online computation because an optimization algorithm is performed
at each sample time to compute the optimal control input
NARMA-L2 Control mdash
This controller requires the least computation of these three
architectures The controller is simply a rearrangement of the neural
network plant model which is trained offline in batch form The only
online computation is a forward pass through the neural network
controller The drawback of this method is that the plant must either be
in companion form or be capable of approximation by a companion
form model
Model Reference Control mdash
The online computation of this controller like NARMA-L2 is
minimal However unlike NARMA-L2 the model reference architecturerequires that a separate neural network controller be trained offline in
addition to the neural network plant model The controller training is
computationally expensive because it requires the use of dynamic back
propagation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2936
29
Fig13 Neural Network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3036
30
Fig14 Testing Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 636
6
In the standard gain scheduled control schemes the gains
selection for the PI controller is dependent on the current pH in the
continuous stirred tank reactor (CSTR) As the pH in the CSTR varies the
gain varies accordingly
In this report use of this control scheme has shown a vast
improvement on the performance of the control system Furthermorethis method does not demand significant computing resources and is
heuristically easy to understand and simple to implement These
characteristics should make this control scheme more appealing to be
put into industrial practice
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 736
7
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 836
8
PROCESS DESCRIPTION AND MODELLING
The practical system under study in this paper is a pH
neutralization system (see Fig 2) The pH is defined as a measure of
acidity or alkalinity of a solution containing water It is mathematically
defined for a dilute solution as the negative decimal logarithm of the
hydrogen ion concentration [H+] in the solution that is
pH = minuslog10[H+] helliphelliphellip (1)
Practical pH processes tends to be very complicated in terms of
variations of the species contained in the influent and the reagent and
the selection of the mixing equipment However there exist several
well known dynamical models accounting for the dominant
characteristics of a pH process in a CSTR The pH neutralization process
presented in this paper was adapted from the dynamical model
presented by McAvoy This model had been derived from first
principles and has been verified by experimental results
The model consists of two parts a dynamical model describing
the flow dynamics of concentrations of the influent compositions intothe CSTR followed by a static non-linearity characterizing the
physiochemical equilibrium conditions between these concentrations
Assuming that the pH neutralization process has two inlet streams the
first inlet stream contains an acid of concentration C 1 with a flow rate
of F 1 and the second inlet stream contains base with concentration C 2
and flow rate of F 2 The dynamic model for the CSTR is then given as
V = F 1C 1 minus (F 1 + F 2 ) ξ helliphelliphelliphelliphellip (2)
V
= F 2C 2 minus (F 1 + F 2 ) ζ helliphelliphelliphelliphelliphellip (3)
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 936
9
where the constant v is the volume of the content in the reactor
and ξ and ζ the concentrations of the acid and the base respectively
These equations describe how the concentrations vary dynamically
with time subject to the input streams F 1 and F 2 To obtain the pH in the
effluent stream a relation between instantaneous concentrations ξ and
ζ is needed
This relationship can be described as a non-linear algebraicequation known as the titration curve Depending on the chemical
species used the titration curve varies In this paper we consider the
case of a weak acid neutralized by a strong base Nominal process
operating conditions are provided in Table 1 Consider an acetic acid
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1036
10
(weak acid) denoted by HAC being neutralized by sodium hydroxide
(strong base) denoted by NaOH
The reactions are
H2OhArr H+
+ OHminus
HAC hArr H+
+ ACminus
NaOH rarr Na+
+ OHminus
The electro neutrality condition states that the sum of the charges of allions in the solution must be zero
this is given by
[Na+] + [H
+] = [OH
minus] + [AC
minus] helliphelliphelliphelliphellip (4)
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1136
11
where the symbol [middot] denotes the concentration of its argument
In water where the dissociation is incomplete we define the
dissociation constant of water as
K w = [H+][OH
minus] helliphelliphelliphelliphellip (5)
where K w = 10minus14
is the dissociation constant for water at 25C Similarly
we can define the dissociation of acetic acid as
K a = [ACminus][H
+] helliphelliphelliphelliphelliphellip (6)
[HAC]
where K a = 18 times 10minus5
is the dissociation constant of acetic acid at 25C
Defining the concentrations of ξ and ζ as
ξ = [HAC] + [ACminus] helliphelliphelliphelliphellip (7)
and
ζ = [Na+] helliphelliphelliphelliphelliphelliphellip (8)
we have a set of seven independent equations (Eqs (2) ndash (8)) with
seven unknowns which describes the dynamic behavior of this
neutralization process A more condensed form of the above equations
can be achieved by eliminating [OHminus] using Eq (5) [AC
minus] using Eq (4)
and [HAC] using Eq (6) The resulting are Eqs (2) (3) and (9)
[H+]
3+ (K a + ζ) [H
+]
2+ (K a(ζ minus ξ) minus K w ) [H
+] minus K wK a = 0 helliphelliphelliphelliphelliphellip (9)
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1236
12
A Simulink model was constructed using this derivation of the
dynamical model to represent the pH neutralization process between
acetic acid and sodium hydroxide (see Fig 3)
Fig 3 Mathematical Model Implementation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1336
13
Fig4 Model for pH Utilization
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1436
14
Fig5 Neutralization Curve simulated in Process Model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1536
15
Fig6 Final Control Model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1636
16
Fig 7 Model Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1736
17
Fig8 NN Predictive Controller
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1836
18
Fig9 Training Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1936
19
Fig10 Neural Network and Training Parameters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2036
20
Fig11 Validation Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2136
21
Fig12 Simulation using Randomly Varying Set point
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2236
22
ARTIFICIAL NEURAL NETWORK
Artificial neural networks are inspired by the early models of
sensory processing by the brain An artificial neural network can becreated by simulating a network of model neurons in a computer By
applying algorithms that mimic the processes of real neurons we can
make the network lsquolearnrsquo to solve many types of problems A model
neuron is referred to as a threshold unit and its function is illustrated in
Figure 1a It receives input from a number of other units or external
sources weighs each input and adds them up If the total input is above
a threshold the output of the unit is one otherwise it is zero
Therefore the output changes from 0 to 1
when the total weighted sum of inputs is equal to the threshold
Learning
If the classification problem is separable we still need a way
to set the weights and the threshold such that the threshold unit
correctly solves the classification problem This can be done in an
iterative manner by presenting examples with known classifications
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2336
23
one after another This process is called learning or training because it
resembles the process we go through when learning something
Simulation of learning by a computer involves making small changes in
the weights and the threshold each time a new example is presented insuch a way that the classification is improved The training can be
implemented by various different algorithms
Back-propagation
Training starts by setting all the weights in the network to small
random numbers Now for each input example the network gives an
output which starts randomly We measure the squared difference
between this output and the desired outputmdashthe correct class or value
The sum of all these numbers over all training examples is called the
total error of the network If this number was zero the network would
be perfect and the smaller the error the better the network
By choosing the weights that minimize the total error one can
obtain the neural network that best solves the problem at hand This isthe same as linear regression where the two parameters characterizing
the line are chosen such that the sum of squared differences between
the line and the data points is minimal In back-propagation the
weights and thresholds are changed each time an example is
presented such that the error gradually becomes smaller This is
repeated often hundreds of times until the error no longer changes
In back-propagation a numerical optimization technique called
gradient descent makes the math particularly simple the form of the
equations gave rise to the name of this method There are some
learning parameters (called learning rate and momentum) that need
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2436
24
tuning when using back-propagation and there are other problems to
consider For instance gradient descent is not guaranteed to find the
global minimum of the error so the result of the training depends on
the initial values of the weights
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2536
25
CONTROL SYSTEMS
Control systems are tightly intertwined in our daily lives so much
so that we take them for granted They may be as low tech andunglamorous as our flush toilet Or they may be as high tech as
electronic fuel injection in our cars that we now drive In fact there is
more than a handful of computer control systems in a typical car that
we now drive In everything from the engine to transmission shock
absorber brakes pollutant emission temperature and so forth there
is an embedded microprocessor controller keeping an eye out for us
The more gadgetry the more tiny controllers pulling the trick
behind our backs1 At the lower end of consumer electronic devices
we can bet on finding at least one embedded microcontroller In the
processing industry controllers play a crucial role in keeping our plants
running ndash virtually everything from simply filling up a storage tank to
complex separation processes and chemical reactors
To consider pH as a controlled variable we use a pH electrode to
measure its value and with a transmitter send the signal to a
controller which can be a little black box or a computer The controller
takes in the pH value and compares it with the desired pH what is
called the set point or the reference If the values are not the same
there is an error and the controller makes proper adjustments by
manipulating the acid or the base pump ndash the actuator
The adjustment is based on calculations made with a control
algorithm also called the control law The error is calculated at the
summing point where we take the desired pH minus the measured pH
Because of how we calculate the error this is a negative-feedback
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2636
26
mechanism When we change a specific operating condition meaning
the set point we would like for example the pH of the bioreactor to
follow our command
This is what we call servo control The pH value of the bioreactor
is subjected to external disturbances (also called load changes) and the
task of suppressing or rejecting the effects of disturbances is called
regulatory control Implementation of a controller may lead to
instability and the issue of system stability is a major concern The
control system also has to be robust such that it is not overly sensitive
to changes in process parameters
Neural Network in Control Systems
Neural networks have been applied successfully in the
identification and control of dynamic systems The universal
approximation capabilities of the multilayer perceptron make it a
popular choice for modeling nonlinear systems and for implementing
general-purpose nonlinear controllers This chapter introduces threepopular neural network architectures for prediction and control that
have been implemented in the Neural Network Toolbox software
Model Predictive Control
NARMA-L2 (or Feedback Linearization) Control
Model Reference Control
This chapter presents brief descriptions of each of these
architectures and demonstrates how you can use them There are
typically two steps involved when using neural networks for control
1 System identification
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2736
27
2 Control design
In the system identification stage you develop a neural network
model of the plant that you want to control In the control design stage
you use the neural network plant model to design (or train) the
controller In each of the three control architectures described in this
chapter the system identification stage is identical The control design
stage however is different for each architecture
bull For model predictive control the plant model is used to predict futur
behavior of the plant and an optimization algorithm is used to select th
control input that optimizes future performance
bull For NARMA-L2 control the controller is simply a rearrangement of th
plant model
bull For model reference control the controller is a neural network that
trained to control a plant so that it follows a reference model The neura
network plant model is used to assist in the controller training
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2836
28
Controllers in NNET Toolbox
Model Predictive Control mdash
This controller uses a neural network model to predict future plant
responses to potential control signals An optimization algorithm then
computes the control signals that optimize future plant performance
The neural network plant model is trained offline in batch form using
any of the training algorithms (This is true for all three control
architectures) The controller however requires a significant amount
of online computation because an optimization algorithm is performed
at each sample time to compute the optimal control input
NARMA-L2 Control mdash
This controller requires the least computation of these three
architectures The controller is simply a rearrangement of the neural
network plant model which is trained offline in batch form The only
online computation is a forward pass through the neural network
controller The drawback of this method is that the plant must either be
in companion form or be capable of approximation by a companion
form model
Model Reference Control mdash
The online computation of this controller like NARMA-L2 is
minimal However unlike NARMA-L2 the model reference architecturerequires that a separate neural network controller be trained offline in
addition to the neural network plant model The controller training is
computationally expensive because it requires the use of dynamic back
propagation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2936
29
Fig13 Neural Network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3036
30
Fig14 Testing Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 736
7
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 836
8
PROCESS DESCRIPTION AND MODELLING
The practical system under study in this paper is a pH
neutralization system (see Fig 2) The pH is defined as a measure of
acidity or alkalinity of a solution containing water It is mathematically
defined for a dilute solution as the negative decimal logarithm of the
hydrogen ion concentration [H+] in the solution that is
pH = minuslog10[H+] helliphelliphellip (1)
Practical pH processes tends to be very complicated in terms of
variations of the species contained in the influent and the reagent and
the selection of the mixing equipment However there exist several
well known dynamical models accounting for the dominant
characteristics of a pH process in a CSTR The pH neutralization process
presented in this paper was adapted from the dynamical model
presented by McAvoy This model had been derived from first
principles and has been verified by experimental results
The model consists of two parts a dynamical model describing
the flow dynamics of concentrations of the influent compositions intothe CSTR followed by a static non-linearity characterizing the
physiochemical equilibrium conditions between these concentrations
Assuming that the pH neutralization process has two inlet streams the
first inlet stream contains an acid of concentration C 1 with a flow rate
of F 1 and the second inlet stream contains base with concentration C 2
and flow rate of F 2 The dynamic model for the CSTR is then given as
V = F 1C 1 minus (F 1 + F 2 ) ξ helliphelliphelliphelliphellip (2)
V
= F 2C 2 minus (F 1 + F 2 ) ζ helliphelliphelliphelliphelliphellip (3)
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 936
9
where the constant v is the volume of the content in the reactor
and ξ and ζ the concentrations of the acid and the base respectively
These equations describe how the concentrations vary dynamically
with time subject to the input streams F 1 and F 2 To obtain the pH in the
effluent stream a relation between instantaneous concentrations ξ and
ζ is needed
This relationship can be described as a non-linear algebraicequation known as the titration curve Depending on the chemical
species used the titration curve varies In this paper we consider the
case of a weak acid neutralized by a strong base Nominal process
operating conditions are provided in Table 1 Consider an acetic acid
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1036
10
(weak acid) denoted by HAC being neutralized by sodium hydroxide
(strong base) denoted by NaOH
The reactions are
H2OhArr H+
+ OHminus
HAC hArr H+
+ ACminus
NaOH rarr Na+
+ OHminus
The electro neutrality condition states that the sum of the charges of allions in the solution must be zero
this is given by
[Na+] + [H
+] = [OH
minus] + [AC
minus] helliphelliphelliphelliphellip (4)
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1136
11
where the symbol [middot] denotes the concentration of its argument
In water where the dissociation is incomplete we define the
dissociation constant of water as
K w = [H+][OH
minus] helliphelliphelliphelliphellip (5)
where K w = 10minus14
is the dissociation constant for water at 25C Similarly
we can define the dissociation of acetic acid as
K a = [ACminus][H
+] helliphelliphelliphelliphelliphellip (6)
[HAC]
where K a = 18 times 10minus5
is the dissociation constant of acetic acid at 25C
Defining the concentrations of ξ and ζ as
ξ = [HAC] + [ACminus] helliphelliphelliphelliphellip (7)
and
ζ = [Na+] helliphelliphelliphelliphelliphelliphellip (8)
we have a set of seven independent equations (Eqs (2) ndash (8)) with
seven unknowns which describes the dynamic behavior of this
neutralization process A more condensed form of the above equations
can be achieved by eliminating [OHminus] using Eq (5) [AC
minus] using Eq (4)
and [HAC] using Eq (6) The resulting are Eqs (2) (3) and (9)
[H+]
3+ (K a + ζ) [H
+]
2+ (K a(ζ minus ξ) minus K w ) [H
+] minus K wK a = 0 helliphelliphelliphelliphelliphellip (9)
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1236
12
A Simulink model was constructed using this derivation of the
dynamical model to represent the pH neutralization process between
acetic acid and sodium hydroxide (see Fig 3)
Fig 3 Mathematical Model Implementation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1336
13
Fig4 Model for pH Utilization
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1436
14
Fig5 Neutralization Curve simulated in Process Model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1536
15
Fig6 Final Control Model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1636
16
Fig 7 Model Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1736
17
Fig8 NN Predictive Controller
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1836
18
Fig9 Training Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1936
19
Fig10 Neural Network and Training Parameters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2036
20
Fig11 Validation Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2136
21
Fig12 Simulation using Randomly Varying Set point
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2236
22
ARTIFICIAL NEURAL NETWORK
Artificial neural networks are inspired by the early models of
sensory processing by the brain An artificial neural network can becreated by simulating a network of model neurons in a computer By
applying algorithms that mimic the processes of real neurons we can
make the network lsquolearnrsquo to solve many types of problems A model
neuron is referred to as a threshold unit and its function is illustrated in
Figure 1a It receives input from a number of other units or external
sources weighs each input and adds them up If the total input is above
a threshold the output of the unit is one otherwise it is zero
Therefore the output changes from 0 to 1
when the total weighted sum of inputs is equal to the threshold
Learning
If the classification problem is separable we still need a way
to set the weights and the threshold such that the threshold unit
correctly solves the classification problem This can be done in an
iterative manner by presenting examples with known classifications
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2336
23
one after another This process is called learning or training because it
resembles the process we go through when learning something
Simulation of learning by a computer involves making small changes in
the weights and the threshold each time a new example is presented insuch a way that the classification is improved The training can be
implemented by various different algorithms
Back-propagation
Training starts by setting all the weights in the network to small
random numbers Now for each input example the network gives an
output which starts randomly We measure the squared difference
between this output and the desired outputmdashthe correct class or value
The sum of all these numbers over all training examples is called the
total error of the network If this number was zero the network would
be perfect and the smaller the error the better the network
By choosing the weights that minimize the total error one can
obtain the neural network that best solves the problem at hand This isthe same as linear regression where the two parameters characterizing
the line are chosen such that the sum of squared differences between
the line and the data points is minimal In back-propagation the
weights and thresholds are changed each time an example is
presented such that the error gradually becomes smaller This is
repeated often hundreds of times until the error no longer changes
In back-propagation a numerical optimization technique called
gradient descent makes the math particularly simple the form of the
equations gave rise to the name of this method There are some
learning parameters (called learning rate and momentum) that need
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2436
24
tuning when using back-propagation and there are other problems to
consider For instance gradient descent is not guaranteed to find the
global minimum of the error so the result of the training depends on
the initial values of the weights
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2536
25
CONTROL SYSTEMS
Control systems are tightly intertwined in our daily lives so much
so that we take them for granted They may be as low tech andunglamorous as our flush toilet Or they may be as high tech as
electronic fuel injection in our cars that we now drive In fact there is
more than a handful of computer control systems in a typical car that
we now drive In everything from the engine to transmission shock
absorber brakes pollutant emission temperature and so forth there
is an embedded microprocessor controller keeping an eye out for us
The more gadgetry the more tiny controllers pulling the trick
behind our backs1 At the lower end of consumer electronic devices
we can bet on finding at least one embedded microcontroller In the
processing industry controllers play a crucial role in keeping our plants
running ndash virtually everything from simply filling up a storage tank to
complex separation processes and chemical reactors
To consider pH as a controlled variable we use a pH electrode to
measure its value and with a transmitter send the signal to a
controller which can be a little black box or a computer The controller
takes in the pH value and compares it with the desired pH what is
called the set point or the reference If the values are not the same
there is an error and the controller makes proper adjustments by
manipulating the acid or the base pump ndash the actuator
The adjustment is based on calculations made with a control
algorithm also called the control law The error is calculated at the
summing point where we take the desired pH minus the measured pH
Because of how we calculate the error this is a negative-feedback
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2636
26
mechanism When we change a specific operating condition meaning
the set point we would like for example the pH of the bioreactor to
follow our command
This is what we call servo control The pH value of the bioreactor
is subjected to external disturbances (also called load changes) and the
task of suppressing or rejecting the effects of disturbances is called
regulatory control Implementation of a controller may lead to
instability and the issue of system stability is a major concern The
control system also has to be robust such that it is not overly sensitive
to changes in process parameters
Neural Network in Control Systems
Neural networks have been applied successfully in the
identification and control of dynamic systems The universal
approximation capabilities of the multilayer perceptron make it a
popular choice for modeling nonlinear systems and for implementing
general-purpose nonlinear controllers This chapter introduces threepopular neural network architectures for prediction and control that
have been implemented in the Neural Network Toolbox software
Model Predictive Control
NARMA-L2 (or Feedback Linearization) Control
Model Reference Control
This chapter presents brief descriptions of each of these
architectures and demonstrates how you can use them There are
typically two steps involved when using neural networks for control
1 System identification
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2736
27
2 Control design
In the system identification stage you develop a neural network
model of the plant that you want to control In the control design stage
you use the neural network plant model to design (or train) the
controller In each of the three control architectures described in this
chapter the system identification stage is identical The control design
stage however is different for each architecture
bull For model predictive control the plant model is used to predict futur
behavior of the plant and an optimization algorithm is used to select th
control input that optimizes future performance
bull For NARMA-L2 control the controller is simply a rearrangement of th
plant model
bull For model reference control the controller is a neural network that
trained to control a plant so that it follows a reference model The neura
network plant model is used to assist in the controller training
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2836
28
Controllers in NNET Toolbox
Model Predictive Control mdash
This controller uses a neural network model to predict future plant
responses to potential control signals An optimization algorithm then
computes the control signals that optimize future plant performance
The neural network plant model is trained offline in batch form using
any of the training algorithms (This is true for all three control
architectures) The controller however requires a significant amount
of online computation because an optimization algorithm is performed
at each sample time to compute the optimal control input
NARMA-L2 Control mdash
This controller requires the least computation of these three
architectures The controller is simply a rearrangement of the neural
network plant model which is trained offline in batch form The only
online computation is a forward pass through the neural network
controller The drawback of this method is that the plant must either be
in companion form or be capable of approximation by a companion
form model
Model Reference Control mdash
The online computation of this controller like NARMA-L2 is
minimal However unlike NARMA-L2 the model reference architecturerequires that a separate neural network controller be trained offline in
addition to the neural network plant model The controller training is
computationally expensive because it requires the use of dynamic back
propagation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2936
29
Fig13 Neural Network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3036
30
Fig14 Testing Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 836
8
PROCESS DESCRIPTION AND MODELLING
The practical system under study in this paper is a pH
neutralization system (see Fig 2) The pH is defined as a measure of
acidity or alkalinity of a solution containing water It is mathematically
defined for a dilute solution as the negative decimal logarithm of the
hydrogen ion concentration [H+] in the solution that is
pH = minuslog10[H+] helliphelliphellip (1)
Practical pH processes tends to be very complicated in terms of
variations of the species contained in the influent and the reagent and
the selection of the mixing equipment However there exist several
well known dynamical models accounting for the dominant
characteristics of a pH process in a CSTR The pH neutralization process
presented in this paper was adapted from the dynamical model
presented by McAvoy This model had been derived from first
principles and has been verified by experimental results
The model consists of two parts a dynamical model describing
the flow dynamics of concentrations of the influent compositions intothe CSTR followed by a static non-linearity characterizing the
physiochemical equilibrium conditions between these concentrations
Assuming that the pH neutralization process has two inlet streams the
first inlet stream contains an acid of concentration C 1 with a flow rate
of F 1 and the second inlet stream contains base with concentration C 2
and flow rate of F 2 The dynamic model for the CSTR is then given as
V = F 1C 1 minus (F 1 + F 2 ) ξ helliphelliphelliphelliphellip (2)
V
= F 2C 2 minus (F 1 + F 2 ) ζ helliphelliphelliphelliphelliphellip (3)
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 936
9
where the constant v is the volume of the content in the reactor
and ξ and ζ the concentrations of the acid and the base respectively
These equations describe how the concentrations vary dynamically
with time subject to the input streams F 1 and F 2 To obtain the pH in the
effluent stream a relation between instantaneous concentrations ξ and
ζ is needed
This relationship can be described as a non-linear algebraicequation known as the titration curve Depending on the chemical
species used the titration curve varies In this paper we consider the
case of a weak acid neutralized by a strong base Nominal process
operating conditions are provided in Table 1 Consider an acetic acid
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1036
10
(weak acid) denoted by HAC being neutralized by sodium hydroxide
(strong base) denoted by NaOH
The reactions are
H2OhArr H+
+ OHminus
HAC hArr H+
+ ACminus
NaOH rarr Na+
+ OHminus
The electro neutrality condition states that the sum of the charges of allions in the solution must be zero
this is given by
[Na+] + [H
+] = [OH
minus] + [AC
minus] helliphelliphelliphelliphellip (4)
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1136
11
where the symbol [middot] denotes the concentration of its argument
In water where the dissociation is incomplete we define the
dissociation constant of water as
K w = [H+][OH
minus] helliphelliphelliphelliphellip (5)
where K w = 10minus14
is the dissociation constant for water at 25C Similarly
we can define the dissociation of acetic acid as
K a = [ACminus][H
+] helliphelliphelliphelliphelliphellip (6)
[HAC]
where K a = 18 times 10minus5
is the dissociation constant of acetic acid at 25C
Defining the concentrations of ξ and ζ as
ξ = [HAC] + [ACminus] helliphelliphelliphelliphellip (7)
and
ζ = [Na+] helliphelliphelliphelliphelliphelliphellip (8)
we have a set of seven independent equations (Eqs (2) ndash (8)) with
seven unknowns which describes the dynamic behavior of this
neutralization process A more condensed form of the above equations
can be achieved by eliminating [OHminus] using Eq (5) [AC
minus] using Eq (4)
and [HAC] using Eq (6) The resulting are Eqs (2) (3) and (9)
[H+]
3+ (K a + ζ) [H
+]
2+ (K a(ζ minus ξ) minus K w ) [H
+] minus K wK a = 0 helliphelliphelliphelliphelliphellip (9)
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1236
12
A Simulink model was constructed using this derivation of the
dynamical model to represent the pH neutralization process between
acetic acid and sodium hydroxide (see Fig 3)
Fig 3 Mathematical Model Implementation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1336
13
Fig4 Model for pH Utilization
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1436
14
Fig5 Neutralization Curve simulated in Process Model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1536
15
Fig6 Final Control Model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1636
16
Fig 7 Model Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1736
17
Fig8 NN Predictive Controller
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1836
18
Fig9 Training Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1936
19
Fig10 Neural Network and Training Parameters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2036
20
Fig11 Validation Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2136
21
Fig12 Simulation using Randomly Varying Set point
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2236
22
ARTIFICIAL NEURAL NETWORK
Artificial neural networks are inspired by the early models of
sensory processing by the brain An artificial neural network can becreated by simulating a network of model neurons in a computer By
applying algorithms that mimic the processes of real neurons we can
make the network lsquolearnrsquo to solve many types of problems A model
neuron is referred to as a threshold unit and its function is illustrated in
Figure 1a It receives input from a number of other units or external
sources weighs each input and adds them up If the total input is above
a threshold the output of the unit is one otherwise it is zero
Therefore the output changes from 0 to 1
when the total weighted sum of inputs is equal to the threshold
Learning
If the classification problem is separable we still need a way
to set the weights and the threshold such that the threshold unit
correctly solves the classification problem This can be done in an
iterative manner by presenting examples with known classifications
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2336
23
one after another This process is called learning or training because it
resembles the process we go through when learning something
Simulation of learning by a computer involves making small changes in
the weights and the threshold each time a new example is presented insuch a way that the classification is improved The training can be
implemented by various different algorithms
Back-propagation
Training starts by setting all the weights in the network to small
random numbers Now for each input example the network gives an
output which starts randomly We measure the squared difference
between this output and the desired outputmdashthe correct class or value
The sum of all these numbers over all training examples is called the
total error of the network If this number was zero the network would
be perfect and the smaller the error the better the network
By choosing the weights that minimize the total error one can
obtain the neural network that best solves the problem at hand This isthe same as linear regression where the two parameters characterizing
the line are chosen such that the sum of squared differences between
the line and the data points is minimal In back-propagation the
weights and thresholds are changed each time an example is
presented such that the error gradually becomes smaller This is
repeated often hundreds of times until the error no longer changes
In back-propagation a numerical optimization technique called
gradient descent makes the math particularly simple the form of the
equations gave rise to the name of this method There are some
learning parameters (called learning rate and momentum) that need
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2436
24
tuning when using back-propagation and there are other problems to
consider For instance gradient descent is not guaranteed to find the
global minimum of the error so the result of the training depends on
the initial values of the weights
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2536
25
CONTROL SYSTEMS
Control systems are tightly intertwined in our daily lives so much
so that we take them for granted They may be as low tech andunglamorous as our flush toilet Or they may be as high tech as
electronic fuel injection in our cars that we now drive In fact there is
more than a handful of computer control systems in a typical car that
we now drive In everything from the engine to transmission shock
absorber brakes pollutant emission temperature and so forth there
is an embedded microprocessor controller keeping an eye out for us
The more gadgetry the more tiny controllers pulling the trick
behind our backs1 At the lower end of consumer electronic devices
we can bet on finding at least one embedded microcontroller In the
processing industry controllers play a crucial role in keeping our plants
running ndash virtually everything from simply filling up a storage tank to
complex separation processes and chemical reactors
To consider pH as a controlled variable we use a pH electrode to
measure its value and with a transmitter send the signal to a
controller which can be a little black box or a computer The controller
takes in the pH value and compares it with the desired pH what is
called the set point or the reference If the values are not the same
there is an error and the controller makes proper adjustments by
manipulating the acid or the base pump ndash the actuator
The adjustment is based on calculations made with a control
algorithm also called the control law The error is calculated at the
summing point where we take the desired pH minus the measured pH
Because of how we calculate the error this is a negative-feedback
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2636
26
mechanism When we change a specific operating condition meaning
the set point we would like for example the pH of the bioreactor to
follow our command
This is what we call servo control The pH value of the bioreactor
is subjected to external disturbances (also called load changes) and the
task of suppressing or rejecting the effects of disturbances is called
regulatory control Implementation of a controller may lead to
instability and the issue of system stability is a major concern The
control system also has to be robust such that it is not overly sensitive
to changes in process parameters
Neural Network in Control Systems
Neural networks have been applied successfully in the
identification and control of dynamic systems The universal
approximation capabilities of the multilayer perceptron make it a
popular choice for modeling nonlinear systems and for implementing
general-purpose nonlinear controllers This chapter introduces threepopular neural network architectures for prediction and control that
have been implemented in the Neural Network Toolbox software
Model Predictive Control
NARMA-L2 (or Feedback Linearization) Control
Model Reference Control
This chapter presents brief descriptions of each of these
architectures and demonstrates how you can use them There are
typically two steps involved when using neural networks for control
1 System identification
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2736
27
2 Control design
In the system identification stage you develop a neural network
model of the plant that you want to control In the control design stage
you use the neural network plant model to design (or train) the
controller In each of the three control architectures described in this
chapter the system identification stage is identical The control design
stage however is different for each architecture
bull For model predictive control the plant model is used to predict futur
behavior of the plant and an optimization algorithm is used to select th
control input that optimizes future performance
bull For NARMA-L2 control the controller is simply a rearrangement of th
plant model
bull For model reference control the controller is a neural network that
trained to control a plant so that it follows a reference model The neura
network plant model is used to assist in the controller training
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2836
28
Controllers in NNET Toolbox
Model Predictive Control mdash
This controller uses a neural network model to predict future plant
responses to potential control signals An optimization algorithm then
computes the control signals that optimize future plant performance
The neural network plant model is trained offline in batch form using
any of the training algorithms (This is true for all three control
architectures) The controller however requires a significant amount
of online computation because an optimization algorithm is performed
at each sample time to compute the optimal control input
NARMA-L2 Control mdash
This controller requires the least computation of these three
architectures The controller is simply a rearrangement of the neural
network plant model which is trained offline in batch form The only
online computation is a forward pass through the neural network
controller The drawback of this method is that the plant must either be
in companion form or be capable of approximation by a companion
form model
Model Reference Control mdash
The online computation of this controller like NARMA-L2 is
minimal However unlike NARMA-L2 the model reference architecturerequires that a separate neural network controller be trained offline in
addition to the neural network plant model The controller training is
computationally expensive because it requires the use of dynamic back
propagation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2936
29
Fig13 Neural Network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3036
30
Fig14 Testing Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 936
9
where the constant v is the volume of the content in the reactor
and ξ and ζ the concentrations of the acid and the base respectively
These equations describe how the concentrations vary dynamically
with time subject to the input streams F 1 and F 2 To obtain the pH in the
effluent stream a relation between instantaneous concentrations ξ and
ζ is needed
This relationship can be described as a non-linear algebraicequation known as the titration curve Depending on the chemical
species used the titration curve varies In this paper we consider the
case of a weak acid neutralized by a strong base Nominal process
operating conditions are provided in Table 1 Consider an acetic acid
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1036
10
(weak acid) denoted by HAC being neutralized by sodium hydroxide
(strong base) denoted by NaOH
The reactions are
H2OhArr H+
+ OHminus
HAC hArr H+
+ ACminus
NaOH rarr Na+
+ OHminus
The electro neutrality condition states that the sum of the charges of allions in the solution must be zero
this is given by
[Na+] + [H
+] = [OH
minus] + [AC
minus] helliphelliphelliphelliphellip (4)
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1136
11
where the symbol [middot] denotes the concentration of its argument
In water where the dissociation is incomplete we define the
dissociation constant of water as
K w = [H+][OH
minus] helliphelliphelliphelliphellip (5)
where K w = 10minus14
is the dissociation constant for water at 25C Similarly
we can define the dissociation of acetic acid as
K a = [ACminus][H
+] helliphelliphelliphelliphelliphellip (6)
[HAC]
where K a = 18 times 10minus5
is the dissociation constant of acetic acid at 25C
Defining the concentrations of ξ and ζ as
ξ = [HAC] + [ACminus] helliphelliphelliphelliphellip (7)
and
ζ = [Na+] helliphelliphelliphelliphelliphelliphellip (8)
we have a set of seven independent equations (Eqs (2) ndash (8)) with
seven unknowns which describes the dynamic behavior of this
neutralization process A more condensed form of the above equations
can be achieved by eliminating [OHminus] using Eq (5) [AC
minus] using Eq (4)
and [HAC] using Eq (6) The resulting are Eqs (2) (3) and (9)
[H+]
3+ (K a + ζ) [H
+]
2+ (K a(ζ minus ξ) minus K w ) [H
+] minus K wK a = 0 helliphelliphelliphelliphelliphellip (9)
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1236
12
A Simulink model was constructed using this derivation of the
dynamical model to represent the pH neutralization process between
acetic acid and sodium hydroxide (see Fig 3)
Fig 3 Mathematical Model Implementation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1336
13
Fig4 Model for pH Utilization
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1436
14
Fig5 Neutralization Curve simulated in Process Model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1536
15
Fig6 Final Control Model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1636
16
Fig 7 Model Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1736
17
Fig8 NN Predictive Controller
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1836
18
Fig9 Training Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1936
19
Fig10 Neural Network and Training Parameters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2036
20
Fig11 Validation Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2136
21
Fig12 Simulation using Randomly Varying Set point
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2236
22
ARTIFICIAL NEURAL NETWORK
Artificial neural networks are inspired by the early models of
sensory processing by the brain An artificial neural network can becreated by simulating a network of model neurons in a computer By
applying algorithms that mimic the processes of real neurons we can
make the network lsquolearnrsquo to solve many types of problems A model
neuron is referred to as a threshold unit and its function is illustrated in
Figure 1a It receives input from a number of other units or external
sources weighs each input and adds them up If the total input is above
a threshold the output of the unit is one otherwise it is zero
Therefore the output changes from 0 to 1
when the total weighted sum of inputs is equal to the threshold
Learning
If the classification problem is separable we still need a way
to set the weights and the threshold such that the threshold unit
correctly solves the classification problem This can be done in an
iterative manner by presenting examples with known classifications
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2336
23
one after another This process is called learning or training because it
resembles the process we go through when learning something
Simulation of learning by a computer involves making small changes in
the weights and the threshold each time a new example is presented insuch a way that the classification is improved The training can be
implemented by various different algorithms
Back-propagation
Training starts by setting all the weights in the network to small
random numbers Now for each input example the network gives an
output which starts randomly We measure the squared difference
between this output and the desired outputmdashthe correct class or value
The sum of all these numbers over all training examples is called the
total error of the network If this number was zero the network would
be perfect and the smaller the error the better the network
By choosing the weights that minimize the total error one can
obtain the neural network that best solves the problem at hand This isthe same as linear regression where the two parameters characterizing
the line are chosen such that the sum of squared differences between
the line and the data points is minimal In back-propagation the
weights and thresholds are changed each time an example is
presented such that the error gradually becomes smaller This is
repeated often hundreds of times until the error no longer changes
In back-propagation a numerical optimization technique called
gradient descent makes the math particularly simple the form of the
equations gave rise to the name of this method There are some
learning parameters (called learning rate and momentum) that need
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2436
24
tuning when using back-propagation and there are other problems to
consider For instance gradient descent is not guaranteed to find the
global minimum of the error so the result of the training depends on
the initial values of the weights
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2536
25
CONTROL SYSTEMS
Control systems are tightly intertwined in our daily lives so much
so that we take them for granted They may be as low tech andunglamorous as our flush toilet Or they may be as high tech as
electronic fuel injection in our cars that we now drive In fact there is
more than a handful of computer control systems in a typical car that
we now drive In everything from the engine to transmission shock
absorber brakes pollutant emission temperature and so forth there
is an embedded microprocessor controller keeping an eye out for us
The more gadgetry the more tiny controllers pulling the trick
behind our backs1 At the lower end of consumer electronic devices
we can bet on finding at least one embedded microcontroller In the
processing industry controllers play a crucial role in keeping our plants
running ndash virtually everything from simply filling up a storage tank to
complex separation processes and chemical reactors
To consider pH as a controlled variable we use a pH electrode to
measure its value and with a transmitter send the signal to a
controller which can be a little black box or a computer The controller
takes in the pH value and compares it with the desired pH what is
called the set point or the reference If the values are not the same
there is an error and the controller makes proper adjustments by
manipulating the acid or the base pump ndash the actuator
The adjustment is based on calculations made with a control
algorithm also called the control law The error is calculated at the
summing point where we take the desired pH minus the measured pH
Because of how we calculate the error this is a negative-feedback
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2636
26
mechanism When we change a specific operating condition meaning
the set point we would like for example the pH of the bioreactor to
follow our command
This is what we call servo control The pH value of the bioreactor
is subjected to external disturbances (also called load changes) and the
task of suppressing or rejecting the effects of disturbances is called
regulatory control Implementation of a controller may lead to
instability and the issue of system stability is a major concern The
control system also has to be robust such that it is not overly sensitive
to changes in process parameters
Neural Network in Control Systems
Neural networks have been applied successfully in the
identification and control of dynamic systems The universal
approximation capabilities of the multilayer perceptron make it a
popular choice for modeling nonlinear systems and for implementing
general-purpose nonlinear controllers This chapter introduces threepopular neural network architectures for prediction and control that
have been implemented in the Neural Network Toolbox software
Model Predictive Control
NARMA-L2 (or Feedback Linearization) Control
Model Reference Control
This chapter presents brief descriptions of each of these
architectures and demonstrates how you can use them There are
typically two steps involved when using neural networks for control
1 System identification
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2736
27
2 Control design
In the system identification stage you develop a neural network
model of the plant that you want to control In the control design stage
you use the neural network plant model to design (or train) the
controller In each of the three control architectures described in this
chapter the system identification stage is identical The control design
stage however is different for each architecture
bull For model predictive control the plant model is used to predict futur
behavior of the plant and an optimization algorithm is used to select th
control input that optimizes future performance
bull For NARMA-L2 control the controller is simply a rearrangement of th
plant model
bull For model reference control the controller is a neural network that
trained to control a plant so that it follows a reference model The neura
network plant model is used to assist in the controller training
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2836
28
Controllers in NNET Toolbox
Model Predictive Control mdash
This controller uses a neural network model to predict future plant
responses to potential control signals An optimization algorithm then
computes the control signals that optimize future plant performance
The neural network plant model is trained offline in batch form using
any of the training algorithms (This is true for all three control
architectures) The controller however requires a significant amount
of online computation because an optimization algorithm is performed
at each sample time to compute the optimal control input
NARMA-L2 Control mdash
This controller requires the least computation of these three
architectures The controller is simply a rearrangement of the neural
network plant model which is trained offline in batch form The only
online computation is a forward pass through the neural network
controller The drawback of this method is that the plant must either be
in companion form or be capable of approximation by a companion
form model
Model Reference Control mdash
The online computation of this controller like NARMA-L2 is
minimal However unlike NARMA-L2 the model reference architecturerequires that a separate neural network controller be trained offline in
addition to the neural network plant model The controller training is
computationally expensive because it requires the use of dynamic back
propagation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2936
29
Fig13 Neural Network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3036
30
Fig14 Testing Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1036
10
(weak acid) denoted by HAC being neutralized by sodium hydroxide
(strong base) denoted by NaOH
The reactions are
H2OhArr H+
+ OHminus
HAC hArr H+
+ ACminus
NaOH rarr Na+
+ OHminus
The electro neutrality condition states that the sum of the charges of allions in the solution must be zero
this is given by
[Na+] + [H
+] = [OH
minus] + [AC
minus] helliphelliphelliphelliphellip (4)
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1136
11
where the symbol [middot] denotes the concentration of its argument
In water where the dissociation is incomplete we define the
dissociation constant of water as
K w = [H+][OH
minus] helliphelliphelliphelliphellip (5)
where K w = 10minus14
is the dissociation constant for water at 25C Similarly
we can define the dissociation of acetic acid as
K a = [ACminus][H
+] helliphelliphelliphelliphelliphellip (6)
[HAC]
where K a = 18 times 10minus5
is the dissociation constant of acetic acid at 25C
Defining the concentrations of ξ and ζ as
ξ = [HAC] + [ACminus] helliphelliphelliphelliphellip (7)
and
ζ = [Na+] helliphelliphelliphelliphelliphelliphellip (8)
we have a set of seven independent equations (Eqs (2) ndash (8)) with
seven unknowns which describes the dynamic behavior of this
neutralization process A more condensed form of the above equations
can be achieved by eliminating [OHminus] using Eq (5) [AC
minus] using Eq (4)
and [HAC] using Eq (6) The resulting are Eqs (2) (3) and (9)
[H+]
3+ (K a + ζ) [H
+]
2+ (K a(ζ minus ξ) minus K w ) [H
+] minus K wK a = 0 helliphelliphelliphelliphelliphellip (9)
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1236
12
A Simulink model was constructed using this derivation of the
dynamical model to represent the pH neutralization process between
acetic acid and sodium hydroxide (see Fig 3)
Fig 3 Mathematical Model Implementation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1336
13
Fig4 Model for pH Utilization
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1436
14
Fig5 Neutralization Curve simulated in Process Model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1536
15
Fig6 Final Control Model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1636
16
Fig 7 Model Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1736
17
Fig8 NN Predictive Controller
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1836
18
Fig9 Training Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1936
19
Fig10 Neural Network and Training Parameters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2036
20
Fig11 Validation Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2136
21
Fig12 Simulation using Randomly Varying Set point
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2236
22
ARTIFICIAL NEURAL NETWORK
Artificial neural networks are inspired by the early models of
sensory processing by the brain An artificial neural network can becreated by simulating a network of model neurons in a computer By
applying algorithms that mimic the processes of real neurons we can
make the network lsquolearnrsquo to solve many types of problems A model
neuron is referred to as a threshold unit and its function is illustrated in
Figure 1a It receives input from a number of other units or external
sources weighs each input and adds them up If the total input is above
a threshold the output of the unit is one otherwise it is zero
Therefore the output changes from 0 to 1
when the total weighted sum of inputs is equal to the threshold
Learning
If the classification problem is separable we still need a way
to set the weights and the threshold such that the threshold unit
correctly solves the classification problem This can be done in an
iterative manner by presenting examples with known classifications
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2336
23
one after another This process is called learning or training because it
resembles the process we go through when learning something
Simulation of learning by a computer involves making small changes in
the weights and the threshold each time a new example is presented insuch a way that the classification is improved The training can be
implemented by various different algorithms
Back-propagation
Training starts by setting all the weights in the network to small
random numbers Now for each input example the network gives an
output which starts randomly We measure the squared difference
between this output and the desired outputmdashthe correct class or value
The sum of all these numbers over all training examples is called the
total error of the network If this number was zero the network would
be perfect and the smaller the error the better the network
By choosing the weights that minimize the total error one can
obtain the neural network that best solves the problem at hand This isthe same as linear regression where the two parameters characterizing
the line are chosen such that the sum of squared differences between
the line and the data points is minimal In back-propagation the
weights and thresholds are changed each time an example is
presented such that the error gradually becomes smaller This is
repeated often hundreds of times until the error no longer changes
In back-propagation a numerical optimization technique called
gradient descent makes the math particularly simple the form of the
equations gave rise to the name of this method There are some
learning parameters (called learning rate and momentum) that need
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2436
24
tuning when using back-propagation and there are other problems to
consider For instance gradient descent is not guaranteed to find the
global minimum of the error so the result of the training depends on
the initial values of the weights
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2536
25
CONTROL SYSTEMS
Control systems are tightly intertwined in our daily lives so much
so that we take them for granted They may be as low tech andunglamorous as our flush toilet Or they may be as high tech as
electronic fuel injection in our cars that we now drive In fact there is
more than a handful of computer control systems in a typical car that
we now drive In everything from the engine to transmission shock
absorber brakes pollutant emission temperature and so forth there
is an embedded microprocessor controller keeping an eye out for us
The more gadgetry the more tiny controllers pulling the trick
behind our backs1 At the lower end of consumer electronic devices
we can bet on finding at least one embedded microcontroller In the
processing industry controllers play a crucial role in keeping our plants
running ndash virtually everything from simply filling up a storage tank to
complex separation processes and chemical reactors
To consider pH as a controlled variable we use a pH electrode to
measure its value and with a transmitter send the signal to a
controller which can be a little black box or a computer The controller
takes in the pH value and compares it with the desired pH what is
called the set point or the reference If the values are not the same
there is an error and the controller makes proper adjustments by
manipulating the acid or the base pump ndash the actuator
The adjustment is based on calculations made with a control
algorithm also called the control law The error is calculated at the
summing point where we take the desired pH minus the measured pH
Because of how we calculate the error this is a negative-feedback
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2636
26
mechanism When we change a specific operating condition meaning
the set point we would like for example the pH of the bioreactor to
follow our command
This is what we call servo control The pH value of the bioreactor
is subjected to external disturbances (also called load changes) and the
task of suppressing or rejecting the effects of disturbances is called
regulatory control Implementation of a controller may lead to
instability and the issue of system stability is a major concern The
control system also has to be robust such that it is not overly sensitive
to changes in process parameters
Neural Network in Control Systems
Neural networks have been applied successfully in the
identification and control of dynamic systems The universal
approximation capabilities of the multilayer perceptron make it a
popular choice for modeling nonlinear systems and for implementing
general-purpose nonlinear controllers This chapter introduces threepopular neural network architectures for prediction and control that
have been implemented in the Neural Network Toolbox software
Model Predictive Control
NARMA-L2 (or Feedback Linearization) Control
Model Reference Control
This chapter presents brief descriptions of each of these
architectures and demonstrates how you can use them There are
typically two steps involved when using neural networks for control
1 System identification
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2736
27
2 Control design
In the system identification stage you develop a neural network
model of the plant that you want to control In the control design stage
you use the neural network plant model to design (or train) the
controller In each of the three control architectures described in this
chapter the system identification stage is identical The control design
stage however is different for each architecture
bull For model predictive control the plant model is used to predict futur
behavior of the plant and an optimization algorithm is used to select th
control input that optimizes future performance
bull For NARMA-L2 control the controller is simply a rearrangement of th
plant model
bull For model reference control the controller is a neural network that
trained to control a plant so that it follows a reference model The neura
network plant model is used to assist in the controller training
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2836
28
Controllers in NNET Toolbox
Model Predictive Control mdash
This controller uses a neural network model to predict future plant
responses to potential control signals An optimization algorithm then
computes the control signals that optimize future plant performance
The neural network plant model is trained offline in batch form using
any of the training algorithms (This is true for all three control
architectures) The controller however requires a significant amount
of online computation because an optimization algorithm is performed
at each sample time to compute the optimal control input
NARMA-L2 Control mdash
This controller requires the least computation of these three
architectures The controller is simply a rearrangement of the neural
network plant model which is trained offline in batch form The only
online computation is a forward pass through the neural network
controller The drawback of this method is that the plant must either be
in companion form or be capable of approximation by a companion
form model
Model Reference Control mdash
The online computation of this controller like NARMA-L2 is
minimal However unlike NARMA-L2 the model reference architecturerequires that a separate neural network controller be trained offline in
addition to the neural network plant model The controller training is
computationally expensive because it requires the use of dynamic back
propagation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2936
29
Fig13 Neural Network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3036
30
Fig14 Testing Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1136
11
where the symbol [middot] denotes the concentration of its argument
In water where the dissociation is incomplete we define the
dissociation constant of water as
K w = [H+][OH
minus] helliphelliphelliphelliphellip (5)
where K w = 10minus14
is the dissociation constant for water at 25C Similarly
we can define the dissociation of acetic acid as
K a = [ACminus][H
+] helliphelliphelliphelliphelliphellip (6)
[HAC]
where K a = 18 times 10minus5
is the dissociation constant of acetic acid at 25C
Defining the concentrations of ξ and ζ as
ξ = [HAC] + [ACminus] helliphelliphelliphelliphellip (7)
and
ζ = [Na+] helliphelliphelliphelliphelliphelliphellip (8)
we have a set of seven independent equations (Eqs (2) ndash (8)) with
seven unknowns which describes the dynamic behavior of this
neutralization process A more condensed form of the above equations
can be achieved by eliminating [OHminus] using Eq (5) [AC
minus] using Eq (4)
and [HAC] using Eq (6) The resulting are Eqs (2) (3) and (9)
[H+]
3+ (K a + ζ) [H
+]
2+ (K a(ζ minus ξ) minus K w ) [H
+] minus K wK a = 0 helliphelliphelliphelliphelliphellip (9)
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1236
12
A Simulink model was constructed using this derivation of the
dynamical model to represent the pH neutralization process between
acetic acid and sodium hydroxide (see Fig 3)
Fig 3 Mathematical Model Implementation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1336
13
Fig4 Model for pH Utilization
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1436
14
Fig5 Neutralization Curve simulated in Process Model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1536
15
Fig6 Final Control Model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1636
16
Fig 7 Model Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1736
17
Fig8 NN Predictive Controller
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1836
18
Fig9 Training Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1936
19
Fig10 Neural Network and Training Parameters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2036
20
Fig11 Validation Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2136
21
Fig12 Simulation using Randomly Varying Set point
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2236
22
ARTIFICIAL NEURAL NETWORK
Artificial neural networks are inspired by the early models of
sensory processing by the brain An artificial neural network can becreated by simulating a network of model neurons in a computer By
applying algorithms that mimic the processes of real neurons we can
make the network lsquolearnrsquo to solve many types of problems A model
neuron is referred to as a threshold unit and its function is illustrated in
Figure 1a It receives input from a number of other units or external
sources weighs each input and adds them up If the total input is above
a threshold the output of the unit is one otherwise it is zero
Therefore the output changes from 0 to 1
when the total weighted sum of inputs is equal to the threshold
Learning
If the classification problem is separable we still need a way
to set the weights and the threshold such that the threshold unit
correctly solves the classification problem This can be done in an
iterative manner by presenting examples with known classifications
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2336
23
one after another This process is called learning or training because it
resembles the process we go through when learning something
Simulation of learning by a computer involves making small changes in
the weights and the threshold each time a new example is presented insuch a way that the classification is improved The training can be
implemented by various different algorithms
Back-propagation
Training starts by setting all the weights in the network to small
random numbers Now for each input example the network gives an
output which starts randomly We measure the squared difference
between this output and the desired outputmdashthe correct class or value
The sum of all these numbers over all training examples is called the
total error of the network If this number was zero the network would
be perfect and the smaller the error the better the network
By choosing the weights that minimize the total error one can
obtain the neural network that best solves the problem at hand This isthe same as linear regression where the two parameters characterizing
the line are chosen such that the sum of squared differences between
the line and the data points is minimal In back-propagation the
weights and thresholds are changed each time an example is
presented such that the error gradually becomes smaller This is
repeated often hundreds of times until the error no longer changes
In back-propagation a numerical optimization technique called
gradient descent makes the math particularly simple the form of the
equations gave rise to the name of this method There are some
learning parameters (called learning rate and momentum) that need
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2436
24
tuning when using back-propagation and there are other problems to
consider For instance gradient descent is not guaranteed to find the
global minimum of the error so the result of the training depends on
the initial values of the weights
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2536
25
CONTROL SYSTEMS
Control systems are tightly intertwined in our daily lives so much
so that we take them for granted They may be as low tech andunglamorous as our flush toilet Or they may be as high tech as
electronic fuel injection in our cars that we now drive In fact there is
more than a handful of computer control systems in a typical car that
we now drive In everything from the engine to transmission shock
absorber brakes pollutant emission temperature and so forth there
is an embedded microprocessor controller keeping an eye out for us
The more gadgetry the more tiny controllers pulling the trick
behind our backs1 At the lower end of consumer electronic devices
we can bet on finding at least one embedded microcontroller In the
processing industry controllers play a crucial role in keeping our plants
running ndash virtually everything from simply filling up a storage tank to
complex separation processes and chemical reactors
To consider pH as a controlled variable we use a pH electrode to
measure its value and with a transmitter send the signal to a
controller which can be a little black box or a computer The controller
takes in the pH value and compares it with the desired pH what is
called the set point or the reference If the values are not the same
there is an error and the controller makes proper adjustments by
manipulating the acid or the base pump ndash the actuator
The adjustment is based on calculations made with a control
algorithm also called the control law The error is calculated at the
summing point where we take the desired pH minus the measured pH
Because of how we calculate the error this is a negative-feedback
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2636
26
mechanism When we change a specific operating condition meaning
the set point we would like for example the pH of the bioreactor to
follow our command
This is what we call servo control The pH value of the bioreactor
is subjected to external disturbances (also called load changes) and the
task of suppressing or rejecting the effects of disturbances is called
regulatory control Implementation of a controller may lead to
instability and the issue of system stability is a major concern The
control system also has to be robust such that it is not overly sensitive
to changes in process parameters
Neural Network in Control Systems
Neural networks have been applied successfully in the
identification and control of dynamic systems The universal
approximation capabilities of the multilayer perceptron make it a
popular choice for modeling nonlinear systems and for implementing
general-purpose nonlinear controllers This chapter introduces threepopular neural network architectures for prediction and control that
have been implemented in the Neural Network Toolbox software
Model Predictive Control
NARMA-L2 (or Feedback Linearization) Control
Model Reference Control
This chapter presents brief descriptions of each of these
architectures and demonstrates how you can use them There are
typically two steps involved when using neural networks for control
1 System identification
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2736
27
2 Control design
In the system identification stage you develop a neural network
model of the plant that you want to control In the control design stage
you use the neural network plant model to design (or train) the
controller In each of the three control architectures described in this
chapter the system identification stage is identical The control design
stage however is different for each architecture
bull For model predictive control the plant model is used to predict futur
behavior of the plant and an optimization algorithm is used to select th
control input that optimizes future performance
bull For NARMA-L2 control the controller is simply a rearrangement of th
plant model
bull For model reference control the controller is a neural network that
trained to control a plant so that it follows a reference model The neura
network plant model is used to assist in the controller training
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2836
28
Controllers in NNET Toolbox
Model Predictive Control mdash
This controller uses a neural network model to predict future plant
responses to potential control signals An optimization algorithm then
computes the control signals that optimize future plant performance
The neural network plant model is trained offline in batch form using
any of the training algorithms (This is true for all three control
architectures) The controller however requires a significant amount
of online computation because an optimization algorithm is performed
at each sample time to compute the optimal control input
NARMA-L2 Control mdash
This controller requires the least computation of these three
architectures The controller is simply a rearrangement of the neural
network plant model which is trained offline in batch form The only
online computation is a forward pass through the neural network
controller The drawback of this method is that the plant must either be
in companion form or be capable of approximation by a companion
form model
Model Reference Control mdash
The online computation of this controller like NARMA-L2 is
minimal However unlike NARMA-L2 the model reference architecturerequires that a separate neural network controller be trained offline in
addition to the neural network plant model The controller training is
computationally expensive because it requires the use of dynamic back
propagation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2936
29
Fig13 Neural Network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3036
30
Fig14 Testing Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1236
12
A Simulink model was constructed using this derivation of the
dynamical model to represent the pH neutralization process between
acetic acid and sodium hydroxide (see Fig 3)
Fig 3 Mathematical Model Implementation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1336
13
Fig4 Model for pH Utilization
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1436
14
Fig5 Neutralization Curve simulated in Process Model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1536
15
Fig6 Final Control Model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1636
16
Fig 7 Model Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1736
17
Fig8 NN Predictive Controller
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1836
18
Fig9 Training Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1936
19
Fig10 Neural Network and Training Parameters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2036
20
Fig11 Validation Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2136
21
Fig12 Simulation using Randomly Varying Set point
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2236
22
ARTIFICIAL NEURAL NETWORK
Artificial neural networks are inspired by the early models of
sensory processing by the brain An artificial neural network can becreated by simulating a network of model neurons in a computer By
applying algorithms that mimic the processes of real neurons we can
make the network lsquolearnrsquo to solve many types of problems A model
neuron is referred to as a threshold unit and its function is illustrated in
Figure 1a It receives input from a number of other units or external
sources weighs each input and adds them up If the total input is above
a threshold the output of the unit is one otherwise it is zero
Therefore the output changes from 0 to 1
when the total weighted sum of inputs is equal to the threshold
Learning
If the classification problem is separable we still need a way
to set the weights and the threshold such that the threshold unit
correctly solves the classification problem This can be done in an
iterative manner by presenting examples with known classifications
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2336
23
one after another This process is called learning or training because it
resembles the process we go through when learning something
Simulation of learning by a computer involves making small changes in
the weights and the threshold each time a new example is presented insuch a way that the classification is improved The training can be
implemented by various different algorithms
Back-propagation
Training starts by setting all the weights in the network to small
random numbers Now for each input example the network gives an
output which starts randomly We measure the squared difference
between this output and the desired outputmdashthe correct class or value
The sum of all these numbers over all training examples is called the
total error of the network If this number was zero the network would
be perfect and the smaller the error the better the network
By choosing the weights that minimize the total error one can
obtain the neural network that best solves the problem at hand This isthe same as linear regression where the two parameters characterizing
the line are chosen such that the sum of squared differences between
the line and the data points is minimal In back-propagation the
weights and thresholds are changed each time an example is
presented such that the error gradually becomes smaller This is
repeated often hundreds of times until the error no longer changes
In back-propagation a numerical optimization technique called
gradient descent makes the math particularly simple the form of the
equations gave rise to the name of this method There are some
learning parameters (called learning rate and momentum) that need
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2436
24
tuning when using back-propagation and there are other problems to
consider For instance gradient descent is not guaranteed to find the
global minimum of the error so the result of the training depends on
the initial values of the weights
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2536
25
CONTROL SYSTEMS
Control systems are tightly intertwined in our daily lives so much
so that we take them for granted They may be as low tech andunglamorous as our flush toilet Or they may be as high tech as
electronic fuel injection in our cars that we now drive In fact there is
more than a handful of computer control systems in a typical car that
we now drive In everything from the engine to transmission shock
absorber brakes pollutant emission temperature and so forth there
is an embedded microprocessor controller keeping an eye out for us
The more gadgetry the more tiny controllers pulling the trick
behind our backs1 At the lower end of consumer electronic devices
we can bet on finding at least one embedded microcontroller In the
processing industry controllers play a crucial role in keeping our plants
running ndash virtually everything from simply filling up a storage tank to
complex separation processes and chemical reactors
To consider pH as a controlled variable we use a pH electrode to
measure its value and with a transmitter send the signal to a
controller which can be a little black box or a computer The controller
takes in the pH value and compares it with the desired pH what is
called the set point or the reference If the values are not the same
there is an error and the controller makes proper adjustments by
manipulating the acid or the base pump ndash the actuator
The adjustment is based on calculations made with a control
algorithm also called the control law The error is calculated at the
summing point where we take the desired pH minus the measured pH
Because of how we calculate the error this is a negative-feedback
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2636
26
mechanism When we change a specific operating condition meaning
the set point we would like for example the pH of the bioreactor to
follow our command
This is what we call servo control The pH value of the bioreactor
is subjected to external disturbances (also called load changes) and the
task of suppressing or rejecting the effects of disturbances is called
regulatory control Implementation of a controller may lead to
instability and the issue of system stability is a major concern The
control system also has to be robust such that it is not overly sensitive
to changes in process parameters
Neural Network in Control Systems
Neural networks have been applied successfully in the
identification and control of dynamic systems The universal
approximation capabilities of the multilayer perceptron make it a
popular choice for modeling nonlinear systems and for implementing
general-purpose nonlinear controllers This chapter introduces threepopular neural network architectures for prediction and control that
have been implemented in the Neural Network Toolbox software
Model Predictive Control
NARMA-L2 (or Feedback Linearization) Control
Model Reference Control
This chapter presents brief descriptions of each of these
architectures and demonstrates how you can use them There are
typically two steps involved when using neural networks for control
1 System identification
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2736
27
2 Control design
In the system identification stage you develop a neural network
model of the plant that you want to control In the control design stage
you use the neural network plant model to design (or train) the
controller In each of the three control architectures described in this
chapter the system identification stage is identical The control design
stage however is different for each architecture
bull For model predictive control the plant model is used to predict futur
behavior of the plant and an optimization algorithm is used to select th
control input that optimizes future performance
bull For NARMA-L2 control the controller is simply a rearrangement of th
plant model
bull For model reference control the controller is a neural network that
trained to control a plant so that it follows a reference model The neura
network plant model is used to assist in the controller training
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2836
28
Controllers in NNET Toolbox
Model Predictive Control mdash
This controller uses a neural network model to predict future plant
responses to potential control signals An optimization algorithm then
computes the control signals that optimize future plant performance
The neural network plant model is trained offline in batch form using
any of the training algorithms (This is true for all three control
architectures) The controller however requires a significant amount
of online computation because an optimization algorithm is performed
at each sample time to compute the optimal control input
NARMA-L2 Control mdash
This controller requires the least computation of these three
architectures The controller is simply a rearrangement of the neural
network plant model which is trained offline in batch form The only
online computation is a forward pass through the neural network
controller The drawback of this method is that the plant must either be
in companion form or be capable of approximation by a companion
form model
Model Reference Control mdash
The online computation of this controller like NARMA-L2 is
minimal However unlike NARMA-L2 the model reference architecturerequires that a separate neural network controller be trained offline in
addition to the neural network plant model The controller training is
computationally expensive because it requires the use of dynamic back
propagation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2936
29
Fig13 Neural Network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3036
30
Fig14 Testing Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1336
13
Fig4 Model for pH Utilization
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1436
14
Fig5 Neutralization Curve simulated in Process Model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1536
15
Fig6 Final Control Model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1636
16
Fig 7 Model Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1736
17
Fig8 NN Predictive Controller
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1836
18
Fig9 Training Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1936
19
Fig10 Neural Network and Training Parameters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2036
20
Fig11 Validation Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2136
21
Fig12 Simulation using Randomly Varying Set point
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2236
22
ARTIFICIAL NEURAL NETWORK
Artificial neural networks are inspired by the early models of
sensory processing by the brain An artificial neural network can becreated by simulating a network of model neurons in a computer By
applying algorithms that mimic the processes of real neurons we can
make the network lsquolearnrsquo to solve many types of problems A model
neuron is referred to as a threshold unit and its function is illustrated in
Figure 1a It receives input from a number of other units or external
sources weighs each input and adds them up If the total input is above
a threshold the output of the unit is one otherwise it is zero
Therefore the output changes from 0 to 1
when the total weighted sum of inputs is equal to the threshold
Learning
If the classification problem is separable we still need a way
to set the weights and the threshold such that the threshold unit
correctly solves the classification problem This can be done in an
iterative manner by presenting examples with known classifications
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2336
23
one after another This process is called learning or training because it
resembles the process we go through when learning something
Simulation of learning by a computer involves making small changes in
the weights and the threshold each time a new example is presented insuch a way that the classification is improved The training can be
implemented by various different algorithms
Back-propagation
Training starts by setting all the weights in the network to small
random numbers Now for each input example the network gives an
output which starts randomly We measure the squared difference
between this output and the desired outputmdashthe correct class or value
The sum of all these numbers over all training examples is called the
total error of the network If this number was zero the network would
be perfect and the smaller the error the better the network
By choosing the weights that minimize the total error one can
obtain the neural network that best solves the problem at hand This isthe same as linear regression where the two parameters characterizing
the line are chosen such that the sum of squared differences between
the line and the data points is minimal In back-propagation the
weights and thresholds are changed each time an example is
presented such that the error gradually becomes smaller This is
repeated often hundreds of times until the error no longer changes
In back-propagation a numerical optimization technique called
gradient descent makes the math particularly simple the form of the
equations gave rise to the name of this method There are some
learning parameters (called learning rate and momentum) that need
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2436
24
tuning when using back-propagation and there are other problems to
consider For instance gradient descent is not guaranteed to find the
global minimum of the error so the result of the training depends on
the initial values of the weights
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2536
25
CONTROL SYSTEMS
Control systems are tightly intertwined in our daily lives so much
so that we take them for granted They may be as low tech andunglamorous as our flush toilet Or they may be as high tech as
electronic fuel injection in our cars that we now drive In fact there is
more than a handful of computer control systems in a typical car that
we now drive In everything from the engine to transmission shock
absorber brakes pollutant emission temperature and so forth there
is an embedded microprocessor controller keeping an eye out for us
The more gadgetry the more tiny controllers pulling the trick
behind our backs1 At the lower end of consumer electronic devices
we can bet on finding at least one embedded microcontroller In the
processing industry controllers play a crucial role in keeping our plants
running ndash virtually everything from simply filling up a storage tank to
complex separation processes and chemical reactors
To consider pH as a controlled variable we use a pH electrode to
measure its value and with a transmitter send the signal to a
controller which can be a little black box or a computer The controller
takes in the pH value and compares it with the desired pH what is
called the set point or the reference If the values are not the same
there is an error and the controller makes proper adjustments by
manipulating the acid or the base pump ndash the actuator
The adjustment is based on calculations made with a control
algorithm also called the control law The error is calculated at the
summing point where we take the desired pH minus the measured pH
Because of how we calculate the error this is a negative-feedback
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2636
26
mechanism When we change a specific operating condition meaning
the set point we would like for example the pH of the bioreactor to
follow our command
This is what we call servo control The pH value of the bioreactor
is subjected to external disturbances (also called load changes) and the
task of suppressing or rejecting the effects of disturbances is called
regulatory control Implementation of a controller may lead to
instability and the issue of system stability is a major concern The
control system also has to be robust such that it is not overly sensitive
to changes in process parameters
Neural Network in Control Systems
Neural networks have been applied successfully in the
identification and control of dynamic systems The universal
approximation capabilities of the multilayer perceptron make it a
popular choice for modeling nonlinear systems and for implementing
general-purpose nonlinear controllers This chapter introduces threepopular neural network architectures for prediction and control that
have been implemented in the Neural Network Toolbox software
Model Predictive Control
NARMA-L2 (or Feedback Linearization) Control
Model Reference Control
This chapter presents brief descriptions of each of these
architectures and demonstrates how you can use them There are
typically two steps involved when using neural networks for control
1 System identification
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2736
27
2 Control design
In the system identification stage you develop a neural network
model of the plant that you want to control In the control design stage
you use the neural network plant model to design (or train) the
controller In each of the three control architectures described in this
chapter the system identification stage is identical The control design
stage however is different for each architecture
bull For model predictive control the plant model is used to predict futur
behavior of the plant and an optimization algorithm is used to select th
control input that optimizes future performance
bull For NARMA-L2 control the controller is simply a rearrangement of th
plant model
bull For model reference control the controller is a neural network that
trained to control a plant so that it follows a reference model The neura
network plant model is used to assist in the controller training
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2836
28
Controllers in NNET Toolbox
Model Predictive Control mdash
This controller uses a neural network model to predict future plant
responses to potential control signals An optimization algorithm then
computes the control signals that optimize future plant performance
The neural network plant model is trained offline in batch form using
any of the training algorithms (This is true for all three control
architectures) The controller however requires a significant amount
of online computation because an optimization algorithm is performed
at each sample time to compute the optimal control input
NARMA-L2 Control mdash
This controller requires the least computation of these three
architectures The controller is simply a rearrangement of the neural
network plant model which is trained offline in batch form The only
online computation is a forward pass through the neural network
controller The drawback of this method is that the plant must either be
in companion form or be capable of approximation by a companion
form model
Model Reference Control mdash
The online computation of this controller like NARMA-L2 is
minimal However unlike NARMA-L2 the model reference architecturerequires that a separate neural network controller be trained offline in
addition to the neural network plant model The controller training is
computationally expensive because it requires the use of dynamic back
propagation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2936
29
Fig13 Neural Network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3036
30
Fig14 Testing Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1436
14
Fig5 Neutralization Curve simulated in Process Model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1536
15
Fig6 Final Control Model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1636
16
Fig 7 Model Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1736
17
Fig8 NN Predictive Controller
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1836
18
Fig9 Training Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1936
19
Fig10 Neural Network and Training Parameters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2036
20
Fig11 Validation Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2136
21
Fig12 Simulation using Randomly Varying Set point
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2236
22
ARTIFICIAL NEURAL NETWORK
Artificial neural networks are inspired by the early models of
sensory processing by the brain An artificial neural network can becreated by simulating a network of model neurons in a computer By
applying algorithms that mimic the processes of real neurons we can
make the network lsquolearnrsquo to solve many types of problems A model
neuron is referred to as a threshold unit and its function is illustrated in
Figure 1a It receives input from a number of other units or external
sources weighs each input and adds them up If the total input is above
a threshold the output of the unit is one otherwise it is zero
Therefore the output changes from 0 to 1
when the total weighted sum of inputs is equal to the threshold
Learning
If the classification problem is separable we still need a way
to set the weights and the threshold such that the threshold unit
correctly solves the classification problem This can be done in an
iterative manner by presenting examples with known classifications
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2336
23
one after another This process is called learning or training because it
resembles the process we go through when learning something
Simulation of learning by a computer involves making small changes in
the weights and the threshold each time a new example is presented insuch a way that the classification is improved The training can be
implemented by various different algorithms
Back-propagation
Training starts by setting all the weights in the network to small
random numbers Now for each input example the network gives an
output which starts randomly We measure the squared difference
between this output and the desired outputmdashthe correct class or value
The sum of all these numbers over all training examples is called the
total error of the network If this number was zero the network would
be perfect and the smaller the error the better the network
By choosing the weights that minimize the total error one can
obtain the neural network that best solves the problem at hand This isthe same as linear regression where the two parameters characterizing
the line are chosen such that the sum of squared differences between
the line and the data points is minimal In back-propagation the
weights and thresholds are changed each time an example is
presented such that the error gradually becomes smaller This is
repeated often hundreds of times until the error no longer changes
In back-propagation a numerical optimization technique called
gradient descent makes the math particularly simple the form of the
equations gave rise to the name of this method There are some
learning parameters (called learning rate and momentum) that need
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2436
24
tuning when using back-propagation and there are other problems to
consider For instance gradient descent is not guaranteed to find the
global minimum of the error so the result of the training depends on
the initial values of the weights
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2536
25
CONTROL SYSTEMS
Control systems are tightly intertwined in our daily lives so much
so that we take them for granted They may be as low tech andunglamorous as our flush toilet Or they may be as high tech as
electronic fuel injection in our cars that we now drive In fact there is
more than a handful of computer control systems in a typical car that
we now drive In everything from the engine to transmission shock
absorber brakes pollutant emission temperature and so forth there
is an embedded microprocessor controller keeping an eye out for us
The more gadgetry the more tiny controllers pulling the trick
behind our backs1 At the lower end of consumer electronic devices
we can bet on finding at least one embedded microcontroller In the
processing industry controllers play a crucial role in keeping our plants
running ndash virtually everything from simply filling up a storage tank to
complex separation processes and chemical reactors
To consider pH as a controlled variable we use a pH electrode to
measure its value and with a transmitter send the signal to a
controller which can be a little black box or a computer The controller
takes in the pH value and compares it with the desired pH what is
called the set point or the reference If the values are not the same
there is an error and the controller makes proper adjustments by
manipulating the acid or the base pump ndash the actuator
The adjustment is based on calculations made with a control
algorithm also called the control law The error is calculated at the
summing point where we take the desired pH minus the measured pH
Because of how we calculate the error this is a negative-feedback
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2636
26
mechanism When we change a specific operating condition meaning
the set point we would like for example the pH of the bioreactor to
follow our command
This is what we call servo control The pH value of the bioreactor
is subjected to external disturbances (also called load changes) and the
task of suppressing or rejecting the effects of disturbances is called
regulatory control Implementation of a controller may lead to
instability and the issue of system stability is a major concern The
control system also has to be robust such that it is not overly sensitive
to changes in process parameters
Neural Network in Control Systems
Neural networks have been applied successfully in the
identification and control of dynamic systems The universal
approximation capabilities of the multilayer perceptron make it a
popular choice for modeling nonlinear systems and for implementing
general-purpose nonlinear controllers This chapter introduces threepopular neural network architectures for prediction and control that
have been implemented in the Neural Network Toolbox software
Model Predictive Control
NARMA-L2 (or Feedback Linearization) Control
Model Reference Control
This chapter presents brief descriptions of each of these
architectures and demonstrates how you can use them There are
typically two steps involved when using neural networks for control
1 System identification
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2736
27
2 Control design
In the system identification stage you develop a neural network
model of the plant that you want to control In the control design stage
you use the neural network plant model to design (or train) the
controller In each of the three control architectures described in this
chapter the system identification stage is identical The control design
stage however is different for each architecture
bull For model predictive control the plant model is used to predict futur
behavior of the plant and an optimization algorithm is used to select th
control input that optimizes future performance
bull For NARMA-L2 control the controller is simply a rearrangement of th
plant model
bull For model reference control the controller is a neural network that
trained to control a plant so that it follows a reference model The neura
network plant model is used to assist in the controller training
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2836
28
Controllers in NNET Toolbox
Model Predictive Control mdash
This controller uses a neural network model to predict future plant
responses to potential control signals An optimization algorithm then
computes the control signals that optimize future plant performance
The neural network plant model is trained offline in batch form using
any of the training algorithms (This is true for all three control
architectures) The controller however requires a significant amount
of online computation because an optimization algorithm is performed
at each sample time to compute the optimal control input
NARMA-L2 Control mdash
This controller requires the least computation of these three
architectures The controller is simply a rearrangement of the neural
network plant model which is trained offline in batch form The only
online computation is a forward pass through the neural network
controller The drawback of this method is that the plant must either be
in companion form or be capable of approximation by a companion
form model
Model Reference Control mdash
The online computation of this controller like NARMA-L2 is
minimal However unlike NARMA-L2 the model reference architecturerequires that a separate neural network controller be trained offline in
addition to the neural network plant model The controller training is
computationally expensive because it requires the use of dynamic back
propagation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2936
29
Fig13 Neural Network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3036
30
Fig14 Testing Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1536
15
Fig6 Final Control Model
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1636
16
Fig 7 Model Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1736
17
Fig8 NN Predictive Controller
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1836
18
Fig9 Training Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1936
19
Fig10 Neural Network and Training Parameters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2036
20
Fig11 Validation Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2136
21
Fig12 Simulation using Randomly Varying Set point
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2236
22
ARTIFICIAL NEURAL NETWORK
Artificial neural networks are inspired by the early models of
sensory processing by the brain An artificial neural network can becreated by simulating a network of model neurons in a computer By
applying algorithms that mimic the processes of real neurons we can
make the network lsquolearnrsquo to solve many types of problems A model
neuron is referred to as a threshold unit and its function is illustrated in
Figure 1a It receives input from a number of other units or external
sources weighs each input and adds them up If the total input is above
a threshold the output of the unit is one otherwise it is zero
Therefore the output changes from 0 to 1
when the total weighted sum of inputs is equal to the threshold
Learning
If the classification problem is separable we still need a way
to set the weights and the threshold such that the threshold unit
correctly solves the classification problem This can be done in an
iterative manner by presenting examples with known classifications
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2336
23
one after another This process is called learning or training because it
resembles the process we go through when learning something
Simulation of learning by a computer involves making small changes in
the weights and the threshold each time a new example is presented insuch a way that the classification is improved The training can be
implemented by various different algorithms
Back-propagation
Training starts by setting all the weights in the network to small
random numbers Now for each input example the network gives an
output which starts randomly We measure the squared difference
between this output and the desired outputmdashthe correct class or value
The sum of all these numbers over all training examples is called the
total error of the network If this number was zero the network would
be perfect and the smaller the error the better the network
By choosing the weights that minimize the total error one can
obtain the neural network that best solves the problem at hand This isthe same as linear regression where the two parameters characterizing
the line are chosen such that the sum of squared differences between
the line and the data points is minimal In back-propagation the
weights and thresholds are changed each time an example is
presented such that the error gradually becomes smaller This is
repeated often hundreds of times until the error no longer changes
In back-propagation a numerical optimization technique called
gradient descent makes the math particularly simple the form of the
equations gave rise to the name of this method There are some
learning parameters (called learning rate and momentum) that need
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2436
24
tuning when using back-propagation and there are other problems to
consider For instance gradient descent is not guaranteed to find the
global minimum of the error so the result of the training depends on
the initial values of the weights
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2536
25
CONTROL SYSTEMS
Control systems are tightly intertwined in our daily lives so much
so that we take them for granted They may be as low tech andunglamorous as our flush toilet Or they may be as high tech as
electronic fuel injection in our cars that we now drive In fact there is
more than a handful of computer control systems in a typical car that
we now drive In everything from the engine to transmission shock
absorber brakes pollutant emission temperature and so forth there
is an embedded microprocessor controller keeping an eye out for us
The more gadgetry the more tiny controllers pulling the trick
behind our backs1 At the lower end of consumer electronic devices
we can bet on finding at least one embedded microcontroller In the
processing industry controllers play a crucial role in keeping our plants
running ndash virtually everything from simply filling up a storage tank to
complex separation processes and chemical reactors
To consider pH as a controlled variable we use a pH electrode to
measure its value and with a transmitter send the signal to a
controller which can be a little black box or a computer The controller
takes in the pH value and compares it with the desired pH what is
called the set point or the reference If the values are not the same
there is an error and the controller makes proper adjustments by
manipulating the acid or the base pump ndash the actuator
The adjustment is based on calculations made with a control
algorithm also called the control law The error is calculated at the
summing point where we take the desired pH minus the measured pH
Because of how we calculate the error this is a negative-feedback
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2636
26
mechanism When we change a specific operating condition meaning
the set point we would like for example the pH of the bioreactor to
follow our command
This is what we call servo control The pH value of the bioreactor
is subjected to external disturbances (also called load changes) and the
task of suppressing or rejecting the effects of disturbances is called
regulatory control Implementation of a controller may lead to
instability and the issue of system stability is a major concern The
control system also has to be robust such that it is not overly sensitive
to changes in process parameters
Neural Network in Control Systems
Neural networks have been applied successfully in the
identification and control of dynamic systems The universal
approximation capabilities of the multilayer perceptron make it a
popular choice for modeling nonlinear systems and for implementing
general-purpose nonlinear controllers This chapter introduces threepopular neural network architectures for prediction and control that
have been implemented in the Neural Network Toolbox software
Model Predictive Control
NARMA-L2 (or Feedback Linearization) Control
Model Reference Control
This chapter presents brief descriptions of each of these
architectures and demonstrates how you can use them There are
typically two steps involved when using neural networks for control
1 System identification
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2736
27
2 Control design
In the system identification stage you develop a neural network
model of the plant that you want to control In the control design stage
you use the neural network plant model to design (or train) the
controller In each of the three control architectures described in this
chapter the system identification stage is identical The control design
stage however is different for each architecture
bull For model predictive control the plant model is used to predict futur
behavior of the plant and an optimization algorithm is used to select th
control input that optimizes future performance
bull For NARMA-L2 control the controller is simply a rearrangement of th
plant model
bull For model reference control the controller is a neural network that
trained to control a plant so that it follows a reference model The neura
network plant model is used to assist in the controller training
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2836
28
Controllers in NNET Toolbox
Model Predictive Control mdash
This controller uses a neural network model to predict future plant
responses to potential control signals An optimization algorithm then
computes the control signals that optimize future plant performance
The neural network plant model is trained offline in batch form using
any of the training algorithms (This is true for all three control
architectures) The controller however requires a significant amount
of online computation because an optimization algorithm is performed
at each sample time to compute the optimal control input
NARMA-L2 Control mdash
This controller requires the least computation of these three
architectures The controller is simply a rearrangement of the neural
network plant model which is trained offline in batch form The only
online computation is a forward pass through the neural network
controller The drawback of this method is that the plant must either be
in companion form or be capable of approximation by a companion
form model
Model Reference Control mdash
The online computation of this controller like NARMA-L2 is
minimal However unlike NARMA-L2 the model reference architecturerequires that a separate neural network controller be trained offline in
addition to the neural network plant model The controller training is
computationally expensive because it requires the use of dynamic back
propagation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2936
29
Fig13 Neural Network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3036
30
Fig14 Testing Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1636
16
Fig 7 Model Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1736
17
Fig8 NN Predictive Controller
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1836
18
Fig9 Training Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1936
19
Fig10 Neural Network and Training Parameters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2036
20
Fig11 Validation Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2136
21
Fig12 Simulation using Randomly Varying Set point
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2236
22
ARTIFICIAL NEURAL NETWORK
Artificial neural networks are inspired by the early models of
sensory processing by the brain An artificial neural network can becreated by simulating a network of model neurons in a computer By
applying algorithms that mimic the processes of real neurons we can
make the network lsquolearnrsquo to solve many types of problems A model
neuron is referred to as a threshold unit and its function is illustrated in
Figure 1a It receives input from a number of other units or external
sources weighs each input and adds them up If the total input is above
a threshold the output of the unit is one otherwise it is zero
Therefore the output changes from 0 to 1
when the total weighted sum of inputs is equal to the threshold
Learning
If the classification problem is separable we still need a way
to set the weights and the threshold such that the threshold unit
correctly solves the classification problem This can be done in an
iterative manner by presenting examples with known classifications
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2336
23
one after another This process is called learning or training because it
resembles the process we go through when learning something
Simulation of learning by a computer involves making small changes in
the weights and the threshold each time a new example is presented insuch a way that the classification is improved The training can be
implemented by various different algorithms
Back-propagation
Training starts by setting all the weights in the network to small
random numbers Now for each input example the network gives an
output which starts randomly We measure the squared difference
between this output and the desired outputmdashthe correct class or value
The sum of all these numbers over all training examples is called the
total error of the network If this number was zero the network would
be perfect and the smaller the error the better the network
By choosing the weights that minimize the total error one can
obtain the neural network that best solves the problem at hand This isthe same as linear regression where the two parameters characterizing
the line are chosen such that the sum of squared differences between
the line and the data points is minimal In back-propagation the
weights and thresholds are changed each time an example is
presented such that the error gradually becomes smaller This is
repeated often hundreds of times until the error no longer changes
In back-propagation a numerical optimization technique called
gradient descent makes the math particularly simple the form of the
equations gave rise to the name of this method There are some
learning parameters (called learning rate and momentum) that need
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2436
24
tuning when using back-propagation and there are other problems to
consider For instance gradient descent is not guaranteed to find the
global minimum of the error so the result of the training depends on
the initial values of the weights
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2536
25
CONTROL SYSTEMS
Control systems are tightly intertwined in our daily lives so much
so that we take them for granted They may be as low tech andunglamorous as our flush toilet Or they may be as high tech as
electronic fuel injection in our cars that we now drive In fact there is
more than a handful of computer control systems in a typical car that
we now drive In everything from the engine to transmission shock
absorber brakes pollutant emission temperature and so forth there
is an embedded microprocessor controller keeping an eye out for us
The more gadgetry the more tiny controllers pulling the trick
behind our backs1 At the lower end of consumer electronic devices
we can bet on finding at least one embedded microcontroller In the
processing industry controllers play a crucial role in keeping our plants
running ndash virtually everything from simply filling up a storage tank to
complex separation processes and chemical reactors
To consider pH as a controlled variable we use a pH electrode to
measure its value and with a transmitter send the signal to a
controller which can be a little black box or a computer The controller
takes in the pH value and compares it with the desired pH what is
called the set point or the reference If the values are not the same
there is an error and the controller makes proper adjustments by
manipulating the acid or the base pump ndash the actuator
The adjustment is based on calculations made with a control
algorithm also called the control law The error is calculated at the
summing point where we take the desired pH minus the measured pH
Because of how we calculate the error this is a negative-feedback
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2636
26
mechanism When we change a specific operating condition meaning
the set point we would like for example the pH of the bioreactor to
follow our command
This is what we call servo control The pH value of the bioreactor
is subjected to external disturbances (also called load changes) and the
task of suppressing or rejecting the effects of disturbances is called
regulatory control Implementation of a controller may lead to
instability and the issue of system stability is a major concern The
control system also has to be robust such that it is not overly sensitive
to changes in process parameters
Neural Network in Control Systems
Neural networks have been applied successfully in the
identification and control of dynamic systems The universal
approximation capabilities of the multilayer perceptron make it a
popular choice for modeling nonlinear systems and for implementing
general-purpose nonlinear controllers This chapter introduces threepopular neural network architectures for prediction and control that
have been implemented in the Neural Network Toolbox software
Model Predictive Control
NARMA-L2 (or Feedback Linearization) Control
Model Reference Control
This chapter presents brief descriptions of each of these
architectures and demonstrates how you can use them There are
typically two steps involved when using neural networks for control
1 System identification
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2736
27
2 Control design
In the system identification stage you develop a neural network
model of the plant that you want to control In the control design stage
you use the neural network plant model to design (or train) the
controller In each of the three control architectures described in this
chapter the system identification stage is identical The control design
stage however is different for each architecture
bull For model predictive control the plant model is used to predict futur
behavior of the plant and an optimization algorithm is used to select th
control input that optimizes future performance
bull For NARMA-L2 control the controller is simply a rearrangement of th
plant model
bull For model reference control the controller is a neural network that
trained to control a plant so that it follows a reference model The neura
network plant model is used to assist in the controller training
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2836
28
Controllers in NNET Toolbox
Model Predictive Control mdash
This controller uses a neural network model to predict future plant
responses to potential control signals An optimization algorithm then
computes the control signals that optimize future plant performance
The neural network plant model is trained offline in batch form using
any of the training algorithms (This is true for all three control
architectures) The controller however requires a significant amount
of online computation because an optimization algorithm is performed
at each sample time to compute the optimal control input
NARMA-L2 Control mdash
This controller requires the least computation of these three
architectures The controller is simply a rearrangement of the neural
network plant model which is trained offline in batch form The only
online computation is a forward pass through the neural network
controller The drawback of this method is that the plant must either be
in companion form or be capable of approximation by a companion
form model
Model Reference Control mdash
The online computation of this controller like NARMA-L2 is
minimal However unlike NARMA-L2 the model reference architecturerequires that a separate neural network controller be trained offline in
addition to the neural network plant model The controller training is
computationally expensive because it requires the use of dynamic back
propagation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2936
29
Fig13 Neural Network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3036
30
Fig14 Testing Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1736
17
Fig8 NN Predictive Controller
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1836
18
Fig9 Training Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1936
19
Fig10 Neural Network and Training Parameters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2036
20
Fig11 Validation Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2136
21
Fig12 Simulation using Randomly Varying Set point
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2236
22
ARTIFICIAL NEURAL NETWORK
Artificial neural networks are inspired by the early models of
sensory processing by the brain An artificial neural network can becreated by simulating a network of model neurons in a computer By
applying algorithms that mimic the processes of real neurons we can
make the network lsquolearnrsquo to solve many types of problems A model
neuron is referred to as a threshold unit and its function is illustrated in
Figure 1a It receives input from a number of other units or external
sources weighs each input and adds them up If the total input is above
a threshold the output of the unit is one otherwise it is zero
Therefore the output changes from 0 to 1
when the total weighted sum of inputs is equal to the threshold
Learning
If the classification problem is separable we still need a way
to set the weights and the threshold such that the threshold unit
correctly solves the classification problem This can be done in an
iterative manner by presenting examples with known classifications
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2336
23
one after another This process is called learning or training because it
resembles the process we go through when learning something
Simulation of learning by a computer involves making small changes in
the weights and the threshold each time a new example is presented insuch a way that the classification is improved The training can be
implemented by various different algorithms
Back-propagation
Training starts by setting all the weights in the network to small
random numbers Now for each input example the network gives an
output which starts randomly We measure the squared difference
between this output and the desired outputmdashthe correct class or value
The sum of all these numbers over all training examples is called the
total error of the network If this number was zero the network would
be perfect and the smaller the error the better the network
By choosing the weights that minimize the total error one can
obtain the neural network that best solves the problem at hand This isthe same as linear regression where the two parameters characterizing
the line are chosen such that the sum of squared differences between
the line and the data points is minimal In back-propagation the
weights and thresholds are changed each time an example is
presented such that the error gradually becomes smaller This is
repeated often hundreds of times until the error no longer changes
In back-propagation a numerical optimization technique called
gradient descent makes the math particularly simple the form of the
equations gave rise to the name of this method There are some
learning parameters (called learning rate and momentum) that need
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2436
24
tuning when using back-propagation and there are other problems to
consider For instance gradient descent is not guaranteed to find the
global minimum of the error so the result of the training depends on
the initial values of the weights
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2536
25
CONTROL SYSTEMS
Control systems are tightly intertwined in our daily lives so much
so that we take them for granted They may be as low tech andunglamorous as our flush toilet Or they may be as high tech as
electronic fuel injection in our cars that we now drive In fact there is
more than a handful of computer control systems in a typical car that
we now drive In everything from the engine to transmission shock
absorber brakes pollutant emission temperature and so forth there
is an embedded microprocessor controller keeping an eye out for us
The more gadgetry the more tiny controllers pulling the trick
behind our backs1 At the lower end of consumer electronic devices
we can bet on finding at least one embedded microcontroller In the
processing industry controllers play a crucial role in keeping our plants
running ndash virtually everything from simply filling up a storage tank to
complex separation processes and chemical reactors
To consider pH as a controlled variable we use a pH electrode to
measure its value and with a transmitter send the signal to a
controller which can be a little black box or a computer The controller
takes in the pH value and compares it with the desired pH what is
called the set point or the reference If the values are not the same
there is an error and the controller makes proper adjustments by
manipulating the acid or the base pump ndash the actuator
The adjustment is based on calculations made with a control
algorithm also called the control law The error is calculated at the
summing point where we take the desired pH minus the measured pH
Because of how we calculate the error this is a negative-feedback
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2636
26
mechanism When we change a specific operating condition meaning
the set point we would like for example the pH of the bioreactor to
follow our command
This is what we call servo control The pH value of the bioreactor
is subjected to external disturbances (also called load changes) and the
task of suppressing or rejecting the effects of disturbances is called
regulatory control Implementation of a controller may lead to
instability and the issue of system stability is a major concern The
control system also has to be robust such that it is not overly sensitive
to changes in process parameters
Neural Network in Control Systems
Neural networks have been applied successfully in the
identification and control of dynamic systems The universal
approximation capabilities of the multilayer perceptron make it a
popular choice for modeling nonlinear systems and for implementing
general-purpose nonlinear controllers This chapter introduces threepopular neural network architectures for prediction and control that
have been implemented in the Neural Network Toolbox software
Model Predictive Control
NARMA-L2 (or Feedback Linearization) Control
Model Reference Control
This chapter presents brief descriptions of each of these
architectures and demonstrates how you can use them There are
typically two steps involved when using neural networks for control
1 System identification
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2736
27
2 Control design
In the system identification stage you develop a neural network
model of the plant that you want to control In the control design stage
you use the neural network plant model to design (or train) the
controller In each of the three control architectures described in this
chapter the system identification stage is identical The control design
stage however is different for each architecture
bull For model predictive control the plant model is used to predict futur
behavior of the plant and an optimization algorithm is used to select th
control input that optimizes future performance
bull For NARMA-L2 control the controller is simply a rearrangement of th
plant model
bull For model reference control the controller is a neural network that
trained to control a plant so that it follows a reference model The neura
network plant model is used to assist in the controller training
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2836
28
Controllers in NNET Toolbox
Model Predictive Control mdash
This controller uses a neural network model to predict future plant
responses to potential control signals An optimization algorithm then
computes the control signals that optimize future plant performance
The neural network plant model is trained offline in batch form using
any of the training algorithms (This is true for all three control
architectures) The controller however requires a significant amount
of online computation because an optimization algorithm is performed
at each sample time to compute the optimal control input
NARMA-L2 Control mdash
This controller requires the least computation of these three
architectures The controller is simply a rearrangement of the neural
network plant model which is trained offline in batch form The only
online computation is a forward pass through the neural network
controller The drawback of this method is that the plant must either be
in companion form or be capable of approximation by a companion
form model
Model Reference Control mdash
The online computation of this controller like NARMA-L2 is
minimal However unlike NARMA-L2 the model reference architecturerequires that a separate neural network controller be trained offline in
addition to the neural network plant model The controller training is
computationally expensive because it requires the use of dynamic back
propagation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2936
29
Fig13 Neural Network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3036
30
Fig14 Testing Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1836
18
Fig9 Training Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1936
19
Fig10 Neural Network and Training Parameters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2036
20
Fig11 Validation Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2136
21
Fig12 Simulation using Randomly Varying Set point
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2236
22
ARTIFICIAL NEURAL NETWORK
Artificial neural networks are inspired by the early models of
sensory processing by the brain An artificial neural network can becreated by simulating a network of model neurons in a computer By
applying algorithms that mimic the processes of real neurons we can
make the network lsquolearnrsquo to solve many types of problems A model
neuron is referred to as a threshold unit and its function is illustrated in
Figure 1a It receives input from a number of other units or external
sources weighs each input and adds them up If the total input is above
a threshold the output of the unit is one otherwise it is zero
Therefore the output changes from 0 to 1
when the total weighted sum of inputs is equal to the threshold
Learning
If the classification problem is separable we still need a way
to set the weights and the threshold such that the threshold unit
correctly solves the classification problem This can be done in an
iterative manner by presenting examples with known classifications
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2336
23
one after another This process is called learning or training because it
resembles the process we go through when learning something
Simulation of learning by a computer involves making small changes in
the weights and the threshold each time a new example is presented insuch a way that the classification is improved The training can be
implemented by various different algorithms
Back-propagation
Training starts by setting all the weights in the network to small
random numbers Now for each input example the network gives an
output which starts randomly We measure the squared difference
between this output and the desired outputmdashthe correct class or value
The sum of all these numbers over all training examples is called the
total error of the network If this number was zero the network would
be perfect and the smaller the error the better the network
By choosing the weights that minimize the total error one can
obtain the neural network that best solves the problem at hand This isthe same as linear regression where the two parameters characterizing
the line are chosen such that the sum of squared differences between
the line and the data points is minimal In back-propagation the
weights and thresholds are changed each time an example is
presented such that the error gradually becomes smaller This is
repeated often hundreds of times until the error no longer changes
In back-propagation a numerical optimization technique called
gradient descent makes the math particularly simple the form of the
equations gave rise to the name of this method There are some
learning parameters (called learning rate and momentum) that need
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2436
24
tuning when using back-propagation and there are other problems to
consider For instance gradient descent is not guaranteed to find the
global minimum of the error so the result of the training depends on
the initial values of the weights
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2536
25
CONTROL SYSTEMS
Control systems are tightly intertwined in our daily lives so much
so that we take them for granted They may be as low tech andunglamorous as our flush toilet Or they may be as high tech as
electronic fuel injection in our cars that we now drive In fact there is
more than a handful of computer control systems in a typical car that
we now drive In everything from the engine to transmission shock
absorber brakes pollutant emission temperature and so forth there
is an embedded microprocessor controller keeping an eye out for us
The more gadgetry the more tiny controllers pulling the trick
behind our backs1 At the lower end of consumer electronic devices
we can bet on finding at least one embedded microcontroller In the
processing industry controllers play a crucial role in keeping our plants
running ndash virtually everything from simply filling up a storage tank to
complex separation processes and chemical reactors
To consider pH as a controlled variable we use a pH electrode to
measure its value and with a transmitter send the signal to a
controller which can be a little black box or a computer The controller
takes in the pH value and compares it with the desired pH what is
called the set point or the reference If the values are not the same
there is an error and the controller makes proper adjustments by
manipulating the acid or the base pump ndash the actuator
The adjustment is based on calculations made with a control
algorithm also called the control law The error is calculated at the
summing point where we take the desired pH minus the measured pH
Because of how we calculate the error this is a negative-feedback
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2636
26
mechanism When we change a specific operating condition meaning
the set point we would like for example the pH of the bioreactor to
follow our command
This is what we call servo control The pH value of the bioreactor
is subjected to external disturbances (also called load changes) and the
task of suppressing or rejecting the effects of disturbances is called
regulatory control Implementation of a controller may lead to
instability and the issue of system stability is a major concern The
control system also has to be robust such that it is not overly sensitive
to changes in process parameters
Neural Network in Control Systems
Neural networks have been applied successfully in the
identification and control of dynamic systems The universal
approximation capabilities of the multilayer perceptron make it a
popular choice for modeling nonlinear systems and for implementing
general-purpose nonlinear controllers This chapter introduces threepopular neural network architectures for prediction and control that
have been implemented in the Neural Network Toolbox software
Model Predictive Control
NARMA-L2 (or Feedback Linearization) Control
Model Reference Control
This chapter presents brief descriptions of each of these
architectures and demonstrates how you can use them There are
typically two steps involved when using neural networks for control
1 System identification
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2736
27
2 Control design
In the system identification stage you develop a neural network
model of the plant that you want to control In the control design stage
you use the neural network plant model to design (or train) the
controller In each of the three control architectures described in this
chapter the system identification stage is identical The control design
stage however is different for each architecture
bull For model predictive control the plant model is used to predict futur
behavior of the plant and an optimization algorithm is used to select th
control input that optimizes future performance
bull For NARMA-L2 control the controller is simply a rearrangement of th
plant model
bull For model reference control the controller is a neural network that
trained to control a plant so that it follows a reference model The neura
network plant model is used to assist in the controller training
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2836
28
Controllers in NNET Toolbox
Model Predictive Control mdash
This controller uses a neural network model to predict future plant
responses to potential control signals An optimization algorithm then
computes the control signals that optimize future plant performance
The neural network plant model is trained offline in batch form using
any of the training algorithms (This is true for all three control
architectures) The controller however requires a significant amount
of online computation because an optimization algorithm is performed
at each sample time to compute the optimal control input
NARMA-L2 Control mdash
This controller requires the least computation of these three
architectures The controller is simply a rearrangement of the neural
network plant model which is trained offline in batch form The only
online computation is a forward pass through the neural network
controller The drawback of this method is that the plant must either be
in companion form or be capable of approximation by a companion
form model
Model Reference Control mdash
The online computation of this controller like NARMA-L2 is
minimal However unlike NARMA-L2 the model reference architecturerequires that a separate neural network controller be trained offline in
addition to the neural network plant model The controller training is
computationally expensive because it requires the use of dynamic back
propagation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2936
29
Fig13 Neural Network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3036
30
Fig14 Testing Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 1936
19
Fig10 Neural Network and Training Parameters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2036
20
Fig11 Validation Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2136
21
Fig12 Simulation using Randomly Varying Set point
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2236
22
ARTIFICIAL NEURAL NETWORK
Artificial neural networks are inspired by the early models of
sensory processing by the brain An artificial neural network can becreated by simulating a network of model neurons in a computer By
applying algorithms that mimic the processes of real neurons we can
make the network lsquolearnrsquo to solve many types of problems A model
neuron is referred to as a threshold unit and its function is illustrated in
Figure 1a It receives input from a number of other units or external
sources weighs each input and adds them up If the total input is above
a threshold the output of the unit is one otherwise it is zero
Therefore the output changes from 0 to 1
when the total weighted sum of inputs is equal to the threshold
Learning
If the classification problem is separable we still need a way
to set the weights and the threshold such that the threshold unit
correctly solves the classification problem This can be done in an
iterative manner by presenting examples with known classifications
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2336
23
one after another This process is called learning or training because it
resembles the process we go through when learning something
Simulation of learning by a computer involves making small changes in
the weights and the threshold each time a new example is presented insuch a way that the classification is improved The training can be
implemented by various different algorithms
Back-propagation
Training starts by setting all the weights in the network to small
random numbers Now for each input example the network gives an
output which starts randomly We measure the squared difference
between this output and the desired outputmdashthe correct class or value
The sum of all these numbers over all training examples is called the
total error of the network If this number was zero the network would
be perfect and the smaller the error the better the network
By choosing the weights that minimize the total error one can
obtain the neural network that best solves the problem at hand This isthe same as linear regression where the two parameters characterizing
the line are chosen such that the sum of squared differences between
the line and the data points is minimal In back-propagation the
weights and thresholds are changed each time an example is
presented such that the error gradually becomes smaller This is
repeated often hundreds of times until the error no longer changes
In back-propagation a numerical optimization technique called
gradient descent makes the math particularly simple the form of the
equations gave rise to the name of this method There are some
learning parameters (called learning rate and momentum) that need
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2436
24
tuning when using back-propagation and there are other problems to
consider For instance gradient descent is not guaranteed to find the
global minimum of the error so the result of the training depends on
the initial values of the weights
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2536
25
CONTROL SYSTEMS
Control systems are tightly intertwined in our daily lives so much
so that we take them for granted They may be as low tech andunglamorous as our flush toilet Or they may be as high tech as
electronic fuel injection in our cars that we now drive In fact there is
more than a handful of computer control systems in a typical car that
we now drive In everything from the engine to transmission shock
absorber brakes pollutant emission temperature and so forth there
is an embedded microprocessor controller keeping an eye out for us
The more gadgetry the more tiny controllers pulling the trick
behind our backs1 At the lower end of consumer electronic devices
we can bet on finding at least one embedded microcontroller In the
processing industry controllers play a crucial role in keeping our plants
running ndash virtually everything from simply filling up a storage tank to
complex separation processes and chemical reactors
To consider pH as a controlled variable we use a pH electrode to
measure its value and with a transmitter send the signal to a
controller which can be a little black box or a computer The controller
takes in the pH value and compares it with the desired pH what is
called the set point or the reference If the values are not the same
there is an error and the controller makes proper adjustments by
manipulating the acid or the base pump ndash the actuator
The adjustment is based on calculations made with a control
algorithm also called the control law The error is calculated at the
summing point where we take the desired pH minus the measured pH
Because of how we calculate the error this is a negative-feedback
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2636
26
mechanism When we change a specific operating condition meaning
the set point we would like for example the pH of the bioreactor to
follow our command
This is what we call servo control The pH value of the bioreactor
is subjected to external disturbances (also called load changes) and the
task of suppressing or rejecting the effects of disturbances is called
regulatory control Implementation of a controller may lead to
instability and the issue of system stability is a major concern The
control system also has to be robust such that it is not overly sensitive
to changes in process parameters
Neural Network in Control Systems
Neural networks have been applied successfully in the
identification and control of dynamic systems The universal
approximation capabilities of the multilayer perceptron make it a
popular choice for modeling nonlinear systems and for implementing
general-purpose nonlinear controllers This chapter introduces threepopular neural network architectures for prediction and control that
have been implemented in the Neural Network Toolbox software
Model Predictive Control
NARMA-L2 (or Feedback Linearization) Control
Model Reference Control
This chapter presents brief descriptions of each of these
architectures and demonstrates how you can use them There are
typically two steps involved when using neural networks for control
1 System identification
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2736
27
2 Control design
In the system identification stage you develop a neural network
model of the plant that you want to control In the control design stage
you use the neural network plant model to design (or train) the
controller In each of the three control architectures described in this
chapter the system identification stage is identical The control design
stage however is different for each architecture
bull For model predictive control the plant model is used to predict futur
behavior of the plant and an optimization algorithm is used to select th
control input that optimizes future performance
bull For NARMA-L2 control the controller is simply a rearrangement of th
plant model
bull For model reference control the controller is a neural network that
trained to control a plant so that it follows a reference model The neura
network plant model is used to assist in the controller training
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2836
28
Controllers in NNET Toolbox
Model Predictive Control mdash
This controller uses a neural network model to predict future plant
responses to potential control signals An optimization algorithm then
computes the control signals that optimize future plant performance
The neural network plant model is trained offline in batch form using
any of the training algorithms (This is true for all three control
architectures) The controller however requires a significant amount
of online computation because an optimization algorithm is performed
at each sample time to compute the optimal control input
NARMA-L2 Control mdash
This controller requires the least computation of these three
architectures The controller is simply a rearrangement of the neural
network plant model which is trained offline in batch form The only
online computation is a forward pass through the neural network
controller The drawback of this method is that the plant must either be
in companion form or be capable of approximation by a companion
form model
Model Reference Control mdash
The online computation of this controller like NARMA-L2 is
minimal However unlike NARMA-L2 the model reference architecturerequires that a separate neural network controller be trained offline in
addition to the neural network plant model The controller training is
computationally expensive because it requires the use of dynamic back
propagation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2936
29
Fig13 Neural Network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3036
30
Fig14 Testing Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2036
20
Fig11 Validation Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2136
21
Fig12 Simulation using Randomly Varying Set point
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2236
22
ARTIFICIAL NEURAL NETWORK
Artificial neural networks are inspired by the early models of
sensory processing by the brain An artificial neural network can becreated by simulating a network of model neurons in a computer By
applying algorithms that mimic the processes of real neurons we can
make the network lsquolearnrsquo to solve many types of problems A model
neuron is referred to as a threshold unit and its function is illustrated in
Figure 1a It receives input from a number of other units or external
sources weighs each input and adds them up If the total input is above
a threshold the output of the unit is one otherwise it is zero
Therefore the output changes from 0 to 1
when the total weighted sum of inputs is equal to the threshold
Learning
If the classification problem is separable we still need a way
to set the weights and the threshold such that the threshold unit
correctly solves the classification problem This can be done in an
iterative manner by presenting examples with known classifications
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2336
23
one after another This process is called learning or training because it
resembles the process we go through when learning something
Simulation of learning by a computer involves making small changes in
the weights and the threshold each time a new example is presented insuch a way that the classification is improved The training can be
implemented by various different algorithms
Back-propagation
Training starts by setting all the weights in the network to small
random numbers Now for each input example the network gives an
output which starts randomly We measure the squared difference
between this output and the desired outputmdashthe correct class or value
The sum of all these numbers over all training examples is called the
total error of the network If this number was zero the network would
be perfect and the smaller the error the better the network
By choosing the weights that minimize the total error one can
obtain the neural network that best solves the problem at hand This isthe same as linear regression where the two parameters characterizing
the line are chosen such that the sum of squared differences between
the line and the data points is minimal In back-propagation the
weights and thresholds are changed each time an example is
presented such that the error gradually becomes smaller This is
repeated often hundreds of times until the error no longer changes
In back-propagation a numerical optimization technique called
gradient descent makes the math particularly simple the form of the
equations gave rise to the name of this method There are some
learning parameters (called learning rate and momentum) that need
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2436
24
tuning when using back-propagation and there are other problems to
consider For instance gradient descent is not guaranteed to find the
global minimum of the error so the result of the training depends on
the initial values of the weights
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2536
25
CONTROL SYSTEMS
Control systems are tightly intertwined in our daily lives so much
so that we take them for granted They may be as low tech andunglamorous as our flush toilet Or they may be as high tech as
electronic fuel injection in our cars that we now drive In fact there is
more than a handful of computer control systems in a typical car that
we now drive In everything from the engine to transmission shock
absorber brakes pollutant emission temperature and so forth there
is an embedded microprocessor controller keeping an eye out for us
The more gadgetry the more tiny controllers pulling the trick
behind our backs1 At the lower end of consumer electronic devices
we can bet on finding at least one embedded microcontroller In the
processing industry controllers play a crucial role in keeping our plants
running ndash virtually everything from simply filling up a storage tank to
complex separation processes and chemical reactors
To consider pH as a controlled variable we use a pH electrode to
measure its value and with a transmitter send the signal to a
controller which can be a little black box or a computer The controller
takes in the pH value and compares it with the desired pH what is
called the set point or the reference If the values are not the same
there is an error and the controller makes proper adjustments by
manipulating the acid or the base pump ndash the actuator
The adjustment is based on calculations made with a control
algorithm also called the control law The error is calculated at the
summing point where we take the desired pH minus the measured pH
Because of how we calculate the error this is a negative-feedback
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2636
26
mechanism When we change a specific operating condition meaning
the set point we would like for example the pH of the bioreactor to
follow our command
This is what we call servo control The pH value of the bioreactor
is subjected to external disturbances (also called load changes) and the
task of suppressing or rejecting the effects of disturbances is called
regulatory control Implementation of a controller may lead to
instability and the issue of system stability is a major concern The
control system also has to be robust such that it is not overly sensitive
to changes in process parameters
Neural Network in Control Systems
Neural networks have been applied successfully in the
identification and control of dynamic systems The universal
approximation capabilities of the multilayer perceptron make it a
popular choice for modeling nonlinear systems and for implementing
general-purpose nonlinear controllers This chapter introduces threepopular neural network architectures for prediction and control that
have been implemented in the Neural Network Toolbox software
Model Predictive Control
NARMA-L2 (or Feedback Linearization) Control
Model Reference Control
This chapter presents brief descriptions of each of these
architectures and demonstrates how you can use them There are
typically two steps involved when using neural networks for control
1 System identification
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2736
27
2 Control design
In the system identification stage you develop a neural network
model of the plant that you want to control In the control design stage
you use the neural network plant model to design (or train) the
controller In each of the three control architectures described in this
chapter the system identification stage is identical The control design
stage however is different for each architecture
bull For model predictive control the plant model is used to predict futur
behavior of the plant and an optimization algorithm is used to select th
control input that optimizes future performance
bull For NARMA-L2 control the controller is simply a rearrangement of th
plant model
bull For model reference control the controller is a neural network that
trained to control a plant so that it follows a reference model The neura
network plant model is used to assist in the controller training
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2836
28
Controllers in NNET Toolbox
Model Predictive Control mdash
This controller uses a neural network model to predict future plant
responses to potential control signals An optimization algorithm then
computes the control signals that optimize future plant performance
The neural network plant model is trained offline in batch form using
any of the training algorithms (This is true for all three control
architectures) The controller however requires a significant amount
of online computation because an optimization algorithm is performed
at each sample time to compute the optimal control input
NARMA-L2 Control mdash
This controller requires the least computation of these three
architectures The controller is simply a rearrangement of the neural
network plant model which is trained offline in batch form The only
online computation is a forward pass through the neural network
controller The drawback of this method is that the plant must either be
in companion form or be capable of approximation by a companion
form model
Model Reference Control mdash
The online computation of this controller like NARMA-L2 is
minimal However unlike NARMA-L2 the model reference architecturerequires that a separate neural network controller be trained offline in
addition to the neural network plant model The controller training is
computationally expensive because it requires the use of dynamic back
propagation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2936
29
Fig13 Neural Network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3036
30
Fig14 Testing Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2136
21
Fig12 Simulation using Randomly Varying Set point
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2236
22
ARTIFICIAL NEURAL NETWORK
Artificial neural networks are inspired by the early models of
sensory processing by the brain An artificial neural network can becreated by simulating a network of model neurons in a computer By
applying algorithms that mimic the processes of real neurons we can
make the network lsquolearnrsquo to solve many types of problems A model
neuron is referred to as a threshold unit and its function is illustrated in
Figure 1a It receives input from a number of other units or external
sources weighs each input and adds them up If the total input is above
a threshold the output of the unit is one otherwise it is zero
Therefore the output changes from 0 to 1
when the total weighted sum of inputs is equal to the threshold
Learning
If the classification problem is separable we still need a way
to set the weights and the threshold such that the threshold unit
correctly solves the classification problem This can be done in an
iterative manner by presenting examples with known classifications
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2336
23
one after another This process is called learning or training because it
resembles the process we go through when learning something
Simulation of learning by a computer involves making small changes in
the weights and the threshold each time a new example is presented insuch a way that the classification is improved The training can be
implemented by various different algorithms
Back-propagation
Training starts by setting all the weights in the network to small
random numbers Now for each input example the network gives an
output which starts randomly We measure the squared difference
between this output and the desired outputmdashthe correct class or value
The sum of all these numbers over all training examples is called the
total error of the network If this number was zero the network would
be perfect and the smaller the error the better the network
By choosing the weights that minimize the total error one can
obtain the neural network that best solves the problem at hand This isthe same as linear regression where the two parameters characterizing
the line are chosen such that the sum of squared differences between
the line and the data points is minimal In back-propagation the
weights and thresholds are changed each time an example is
presented such that the error gradually becomes smaller This is
repeated often hundreds of times until the error no longer changes
In back-propagation a numerical optimization technique called
gradient descent makes the math particularly simple the form of the
equations gave rise to the name of this method There are some
learning parameters (called learning rate and momentum) that need
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2436
24
tuning when using back-propagation and there are other problems to
consider For instance gradient descent is not guaranteed to find the
global minimum of the error so the result of the training depends on
the initial values of the weights
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2536
25
CONTROL SYSTEMS
Control systems are tightly intertwined in our daily lives so much
so that we take them for granted They may be as low tech andunglamorous as our flush toilet Or they may be as high tech as
electronic fuel injection in our cars that we now drive In fact there is
more than a handful of computer control systems in a typical car that
we now drive In everything from the engine to transmission shock
absorber brakes pollutant emission temperature and so forth there
is an embedded microprocessor controller keeping an eye out for us
The more gadgetry the more tiny controllers pulling the trick
behind our backs1 At the lower end of consumer electronic devices
we can bet on finding at least one embedded microcontroller In the
processing industry controllers play a crucial role in keeping our plants
running ndash virtually everything from simply filling up a storage tank to
complex separation processes and chemical reactors
To consider pH as a controlled variable we use a pH electrode to
measure its value and with a transmitter send the signal to a
controller which can be a little black box or a computer The controller
takes in the pH value and compares it with the desired pH what is
called the set point or the reference If the values are not the same
there is an error and the controller makes proper adjustments by
manipulating the acid or the base pump ndash the actuator
The adjustment is based on calculations made with a control
algorithm also called the control law The error is calculated at the
summing point where we take the desired pH minus the measured pH
Because of how we calculate the error this is a negative-feedback
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2636
26
mechanism When we change a specific operating condition meaning
the set point we would like for example the pH of the bioreactor to
follow our command
This is what we call servo control The pH value of the bioreactor
is subjected to external disturbances (also called load changes) and the
task of suppressing or rejecting the effects of disturbances is called
regulatory control Implementation of a controller may lead to
instability and the issue of system stability is a major concern The
control system also has to be robust such that it is not overly sensitive
to changes in process parameters
Neural Network in Control Systems
Neural networks have been applied successfully in the
identification and control of dynamic systems The universal
approximation capabilities of the multilayer perceptron make it a
popular choice for modeling nonlinear systems and for implementing
general-purpose nonlinear controllers This chapter introduces threepopular neural network architectures for prediction and control that
have been implemented in the Neural Network Toolbox software
Model Predictive Control
NARMA-L2 (or Feedback Linearization) Control
Model Reference Control
This chapter presents brief descriptions of each of these
architectures and demonstrates how you can use them There are
typically two steps involved when using neural networks for control
1 System identification
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2736
27
2 Control design
In the system identification stage you develop a neural network
model of the plant that you want to control In the control design stage
you use the neural network plant model to design (or train) the
controller In each of the three control architectures described in this
chapter the system identification stage is identical The control design
stage however is different for each architecture
bull For model predictive control the plant model is used to predict futur
behavior of the plant and an optimization algorithm is used to select th
control input that optimizes future performance
bull For NARMA-L2 control the controller is simply a rearrangement of th
plant model
bull For model reference control the controller is a neural network that
trained to control a plant so that it follows a reference model The neura
network plant model is used to assist in the controller training
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2836
28
Controllers in NNET Toolbox
Model Predictive Control mdash
This controller uses a neural network model to predict future plant
responses to potential control signals An optimization algorithm then
computes the control signals that optimize future plant performance
The neural network plant model is trained offline in batch form using
any of the training algorithms (This is true for all three control
architectures) The controller however requires a significant amount
of online computation because an optimization algorithm is performed
at each sample time to compute the optimal control input
NARMA-L2 Control mdash
This controller requires the least computation of these three
architectures The controller is simply a rearrangement of the neural
network plant model which is trained offline in batch form The only
online computation is a forward pass through the neural network
controller The drawback of this method is that the plant must either be
in companion form or be capable of approximation by a companion
form model
Model Reference Control mdash
The online computation of this controller like NARMA-L2 is
minimal However unlike NARMA-L2 the model reference architecturerequires that a separate neural network controller be trained offline in
addition to the neural network plant model The controller training is
computationally expensive because it requires the use of dynamic back
propagation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2936
29
Fig13 Neural Network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3036
30
Fig14 Testing Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2236
22
ARTIFICIAL NEURAL NETWORK
Artificial neural networks are inspired by the early models of
sensory processing by the brain An artificial neural network can becreated by simulating a network of model neurons in a computer By
applying algorithms that mimic the processes of real neurons we can
make the network lsquolearnrsquo to solve many types of problems A model
neuron is referred to as a threshold unit and its function is illustrated in
Figure 1a It receives input from a number of other units or external
sources weighs each input and adds them up If the total input is above
a threshold the output of the unit is one otherwise it is zero
Therefore the output changes from 0 to 1
when the total weighted sum of inputs is equal to the threshold
Learning
If the classification problem is separable we still need a way
to set the weights and the threshold such that the threshold unit
correctly solves the classification problem This can be done in an
iterative manner by presenting examples with known classifications
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2336
23
one after another This process is called learning or training because it
resembles the process we go through when learning something
Simulation of learning by a computer involves making small changes in
the weights and the threshold each time a new example is presented insuch a way that the classification is improved The training can be
implemented by various different algorithms
Back-propagation
Training starts by setting all the weights in the network to small
random numbers Now for each input example the network gives an
output which starts randomly We measure the squared difference
between this output and the desired outputmdashthe correct class or value
The sum of all these numbers over all training examples is called the
total error of the network If this number was zero the network would
be perfect and the smaller the error the better the network
By choosing the weights that minimize the total error one can
obtain the neural network that best solves the problem at hand This isthe same as linear regression where the two parameters characterizing
the line are chosen such that the sum of squared differences between
the line and the data points is minimal In back-propagation the
weights and thresholds are changed each time an example is
presented such that the error gradually becomes smaller This is
repeated often hundreds of times until the error no longer changes
In back-propagation a numerical optimization technique called
gradient descent makes the math particularly simple the form of the
equations gave rise to the name of this method There are some
learning parameters (called learning rate and momentum) that need
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2436
24
tuning when using back-propagation and there are other problems to
consider For instance gradient descent is not guaranteed to find the
global minimum of the error so the result of the training depends on
the initial values of the weights
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2536
25
CONTROL SYSTEMS
Control systems are tightly intertwined in our daily lives so much
so that we take them for granted They may be as low tech andunglamorous as our flush toilet Or they may be as high tech as
electronic fuel injection in our cars that we now drive In fact there is
more than a handful of computer control systems in a typical car that
we now drive In everything from the engine to transmission shock
absorber brakes pollutant emission temperature and so forth there
is an embedded microprocessor controller keeping an eye out for us
The more gadgetry the more tiny controllers pulling the trick
behind our backs1 At the lower end of consumer electronic devices
we can bet on finding at least one embedded microcontroller In the
processing industry controllers play a crucial role in keeping our plants
running ndash virtually everything from simply filling up a storage tank to
complex separation processes and chemical reactors
To consider pH as a controlled variable we use a pH electrode to
measure its value and with a transmitter send the signal to a
controller which can be a little black box or a computer The controller
takes in the pH value and compares it with the desired pH what is
called the set point or the reference If the values are not the same
there is an error and the controller makes proper adjustments by
manipulating the acid or the base pump ndash the actuator
The adjustment is based on calculations made with a control
algorithm also called the control law The error is calculated at the
summing point where we take the desired pH minus the measured pH
Because of how we calculate the error this is a negative-feedback
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2636
26
mechanism When we change a specific operating condition meaning
the set point we would like for example the pH of the bioreactor to
follow our command
This is what we call servo control The pH value of the bioreactor
is subjected to external disturbances (also called load changes) and the
task of suppressing or rejecting the effects of disturbances is called
regulatory control Implementation of a controller may lead to
instability and the issue of system stability is a major concern The
control system also has to be robust such that it is not overly sensitive
to changes in process parameters
Neural Network in Control Systems
Neural networks have been applied successfully in the
identification and control of dynamic systems The universal
approximation capabilities of the multilayer perceptron make it a
popular choice for modeling nonlinear systems and for implementing
general-purpose nonlinear controllers This chapter introduces threepopular neural network architectures for prediction and control that
have been implemented in the Neural Network Toolbox software
Model Predictive Control
NARMA-L2 (or Feedback Linearization) Control
Model Reference Control
This chapter presents brief descriptions of each of these
architectures and demonstrates how you can use them There are
typically two steps involved when using neural networks for control
1 System identification
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2736
27
2 Control design
In the system identification stage you develop a neural network
model of the plant that you want to control In the control design stage
you use the neural network plant model to design (or train) the
controller In each of the three control architectures described in this
chapter the system identification stage is identical The control design
stage however is different for each architecture
bull For model predictive control the plant model is used to predict futur
behavior of the plant and an optimization algorithm is used to select th
control input that optimizes future performance
bull For NARMA-L2 control the controller is simply a rearrangement of th
plant model
bull For model reference control the controller is a neural network that
trained to control a plant so that it follows a reference model The neura
network plant model is used to assist in the controller training
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2836
28
Controllers in NNET Toolbox
Model Predictive Control mdash
This controller uses a neural network model to predict future plant
responses to potential control signals An optimization algorithm then
computes the control signals that optimize future plant performance
The neural network plant model is trained offline in batch form using
any of the training algorithms (This is true for all three control
architectures) The controller however requires a significant amount
of online computation because an optimization algorithm is performed
at each sample time to compute the optimal control input
NARMA-L2 Control mdash
This controller requires the least computation of these three
architectures The controller is simply a rearrangement of the neural
network plant model which is trained offline in batch form The only
online computation is a forward pass through the neural network
controller The drawback of this method is that the plant must either be
in companion form or be capable of approximation by a companion
form model
Model Reference Control mdash
The online computation of this controller like NARMA-L2 is
minimal However unlike NARMA-L2 the model reference architecturerequires that a separate neural network controller be trained offline in
addition to the neural network plant model The controller training is
computationally expensive because it requires the use of dynamic back
propagation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2936
29
Fig13 Neural Network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3036
30
Fig14 Testing Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2336
23
one after another This process is called learning or training because it
resembles the process we go through when learning something
Simulation of learning by a computer involves making small changes in
the weights and the threshold each time a new example is presented insuch a way that the classification is improved The training can be
implemented by various different algorithms
Back-propagation
Training starts by setting all the weights in the network to small
random numbers Now for each input example the network gives an
output which starts randomly We measure the squared difference
between this output and the desired outputmdashthe correct class or value
The sum of all these numbers over all training examples is called the
total error of the network If this number was zero the network would
be perfect and the smaller the error the better the network
By choosing the weights that minimize the total error one can
obtain the neural network that best solves the problem at hand This isthe same as linear regression where the two parameters characterizing
the line are chosen such that the sum of squared differences between
the line and the data points is minimal In back-propagation the
weights and thresholds are changed each time an example is
presented such that the error gradually becomes smaller This is
repeated often hundreds of times until the error no longer changes
In back-propagation a numerical optimization technique called
gradient descent makes the math particularly simple the form of the
equations gave rise to the name of this method There are some
learning parameters (called learning rate and momentum) that need
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2436
24
tuning when using back-propagation and there are other problems to
consider For instance gradient descent is not guaranteed to find the
global minimum of the error so the result of the training depends on
the initial values of the weights
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2536
25
CONTROL SYSTEMS
Control systems are tightly intertwined in our daily lives so much
so that we take them for granted They may be as low tech andunglamorous as our flush toilet Or they may be as high tech as
electronic fuel injection in our cars that we now drive In fact there is
more than a handful of computer control systems in a typical car that
we now drive In everything from the engine to transmission shock
absorber brakes pollutant emission temperature and so forth there
is an embedded microprocessor controller keeping an eye out for us
The more gadgetry the more tiny controllers pulling the trick
behind our backs1 At the lower end of consumer electronic devices
we can bet on finding at least one embedded microcontroller In the
processing industry controllers play a crucial role in keeping our plants
running ndash virtually everything from simply filling up a storage tank to
complex separation processes and chemical reactors
To consider pH as a controlled variable we use a pH electrode to
measure its value and with a transmitter send the signal to a
controller which can be a little black box or a computer The controller
takes in the pH value and compares it with the desired pH what is
called the set point or the reference If the values are not the same
there is an error and the controller makes proper adjustments by
manipulating the acid or the base pump ndash the actuator
The adjustment is based on calculations made with a control
algorithm also called the control law The error is calculated at the
summing point where we take the desired pH minus the measured pH
Because of how we calculate the error this is a negative-feedback
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2636
26
mechanism When we change a specific operating condition meaning
the set point we would like for example the pH of the bioreactor to
follow our command
This is what we call servo control The pH value of the bioreactor
is subjected to external disturbances (also called load changes) and the
task of suppressing or rejecting the effects of disturbances is called
regulatory control Implementation of a controller may lead to
instability and the issue of system stability is a major concern The
control system also has to be robust such that it is not overly sensitive
to changes in process parameters
Neural Network in Control Systems
Neural networks have been applied successfully in the
identification and control of dynamic systems The universal
approximation capabilities of the multilayer perceptron make it a
popular choice for modeling nonlinear systems and for implementing
general-purpose nonlinear controllers This chapter introduces threepopular neural network architectures for prediction and control that
have been implemented in the Neural Network Toolbox software
Model Predictive Control
NARMA-L2 (or Feedback Linearization) Control
Model Reference Control
This chapter presents brief descriptions of each of these
architectures and demonstrates how you can use them There are
typically two steps involved when using neural networks for control
1 System identification
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2736
27
2 Control design
In the system identification stage you develop a neural network
model of the plant that you want to control In the control design stage
you use the neural network plant model to design (or train) the
controller In each of the three control architectures described in this
chapter the system identification stage is identical The control design
stage however is different for each architecture
bull For model predictive control the plant model is used to predict futur
behavior of the plant and an optimization algorithm is used to select th
control input that optimizes future performance
bull For NARMA-L2 control the controller is simply a rearrangement of th
plant model
bull For model reference control the controller is a neural network that
trained to control a plant so that it follows a reference model The neura
network plant model is used to assist in the controller training
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2836
28
Controllers in NNET Toolbox
Model Predictive Control mdash
This controller uses a neural network model to predict future plant
responses to potential control signals An optimization algorithm then
computes the control signals that optimize future plant performance
The neural network plant model is trained offline in batch form using
any of the training algorithms (This is true for all three control
architectures) The controller however requires a significant amount
of online computation because an optimization algorithm is performed
at each sample time to compute the optimal control input
NARMA-L2 Control mdash
This controller requires the least computation of these three
architectures The controller is simply a rearrangement of the neural
network plant model which is trained offline in batch form The only
online computation is a forward pass through the neural network
controller The drawback of this method is that the plant must either be
in companion form or be capable of approximation by a companion
form model
Model Reference Control mdash
The online computation of this controller like NARMA-L2 is
minimal However unlike NARMA-L2 the model reference architecturerequires that a separate neural network controller be trained offline in
addition to the neural network plant model The controller training is
computationally expensive because it requires the use of dynamic back
propagation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2936
29
Fig13 Neural Network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3036
30
Fig14 Testing Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2436
24
tuning when using back-propagation and there are other problems to
consider For instance gradient descent is not guaranteed to find the
global minimum of the error so the result of the training depends on
the initial values of the weights
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2536
25
CONTROL SYSTEMS
Control systems are tightly intertwined in our daily lives so much
so that we take them for granted They may be as low tech andunglamorous as our flush toilet Or they may be as high tech as
electronic fuel injection in our cars that we now drive In fact there is
more than a handful of computer control systems in a typical car that
we now drive In everything from the engine to transmission shock
absorber brakes pollutant emission temperature and so forth there
is an embedded microprocessor controller keeping an eye out for us
The more gadgetry the more tiny controllers pulling the trick
behind our backs1 At the lower end of consumer electronic devices
we can bet on finding at least one embedded microcontroller In the
processing industry controllers play a crucial role in keeping our plants
running ndash virtually everything from simply filling up a storage tank to
complex separation processes and chemical reactors
To consider pH as a controlled variable we use a pH electrode to
measure its value and with a transmitter send the signal to a
controller which can be a little black box or a computer The controller
takes in the pH value and compares it with the desired pH what is
called the set point or the reference If the values are not the same
there is an error and the controller makes proper adjustments by
manipulating the acid or the base pump ndash the actuator
The adjustment is based on calculations made with a control
algorithm also called the control law The error is calculated at the
summing point where we take the desired pH minus the measured pH
Because of how we calculate the error this is a negative-feedback
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2636
26
mechanism When we change a specific operating condition meaning
the set point we would like for example the pH of the bioreactor to
follow our command
This is what we call servo control The pH value of the bioreactor
is subjected to external disturbances (also called load changes) and the
task of suppressing or rejecting the effects of disturbances is called
regulatory control Implementation of a controller may lead to
instability and the issue of system stability is a major concern The
control system also has to be robust such that it is not overly sensitive
to changes in process parameters
Neural Network in Control Systems
Neural networks have been applied successfully in the
identification and control of dynamic systems The universal
approximation capabilities of the multilayer perceptron make it a
popular choice for modeling nonlinear systems and for implementing
general-purpose nonlinear controllers This chapter introduces threepopular neural network architectures for prediction and control that
have been implemented in the Neural Network Toolbox software
Model Predictive Control
NARMA-L2 (or Feedback Linearization) Control
Model Reference Control
This chapter presents brief descriptions of each of these
architectures and demonstrates how you can use them There are
typically two steps involved when using neural networks for control
1 System identification
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2736
27
2 Control design
In the system identification stage you develop a neural network
model of the plant that you want to control In the control design stage
you use the neural network plant model to design (or train) the
controller In each of the three control architectures described in this
chapter the system identification stage is identical The control design
stage however is different for each architecture
bull For model predictive control the plant model is used to predict futur
behavior of the plant and an optimization algorithm is used to select th
control input that optimizes future performance
bull For NARMA-L2 control the controller is simply a rearrangement of th
plant model
bull For model reference control the controller is a neural network that
trained to control a plant so that it follows a reference model The neura
network plant model is used to assist in the controller training
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2836
28
Controllers in NNET Toolbox
Model Predictive Control mdash
This controller uses a neural network model to predict future plant
responses to potential control signals An optimization algorithm then
computes the control signals that optimize future plant performance
The neural network plant model is trained offline in batch form using
any of the training algorithms (This is true for all three control
architectures) The controller however requires a significant amount
of online computation because an optimization algorithm is performed
at each sample time to compute the optimal control input
NARMA-L2 Control mdash
This controller requires the least computation of these three
architectures The controller is simply a rearrangement of the neural
network plant model which is trained offline in batch form The only
online computation is a forward pass through the neural network
controller The drawback of this method is that the plant must either be
in companion form or be capable of approximation by a companion
form model
Model Reference Control mdash
The online computation of this controller like NARMA-L2 is
minimal However unlike NARMA-L2 the model reference architecturerequires that a separate neural network controller be trained offline in
addition to the neural network plant model The controller training is
computationally expensive because it requires the use of dynamic back
propagation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2936
29
Fig13 Neural Network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3036
30
Fig14 Testing Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2536
25
CONTROL SYSTEMS
Control systems are tightly intertwined in our daily lives so much
so that we take them for granted They may be as low tech andunglamorous as our flush toilet Or they may be as high tech as
electronic fuel injection in our cars that we now drive In fact there is
more than a handful of computer control systems in a typical car that
we now drive In everything from the engine to transmission shock
absorber brakes pollutant emission temperature and so forth there
is an embedded microprocessor controller keeping an eye out for us
The more gadgetry the more tiny controllers pulling the trick
behind our backs1 At the lower end of consumer electronic devices
we can bet on finding at least one embedded microcontroller In the
processing industry controllers play a crucial role in keeping our plants
running ndash virtually everything from simply filling up a storage tank to
complex separation processes and chemical reactors
To consider pH as a controlled variable we use a pH electrode to
measure its value and with a transmitter send the signal to a
controller which can be a little black box or a computer The controller
takes in the pH value and compares it with the desired pH what is
called the set point or the reference If the values are not the same
there is an error and the controller makes proper adjustments by
manipulating the acid or the base pump ndash the actuator
The adjustment is based on calculations made with a control
algorithm also called the control law The error is calculated at the
summing point where we take the desired pH minus the measured pH
Because of how we calculate the error this is a negative-feedback
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2636
26
mechanism When we change a specific operating condition meaning
the set point we would like for example the pH of the bioreactor to
follow our command
This is what we call servo control The pH value of the bioreactor
is subjected to external disturbances (also called load changes) and the
task of suppressing or rejecting the effects of disturbances is called
regulatory control Implementation of a controller may lead to
instability and the issue of system stability is a major concern The
control system also has to be robust such that it is not overly sensitive
to changes in process parameters
Neural Network in Control Systems
Neural networks have been applied successfully in the
identification and control of dynamic systems The universal
approximation capabilities of the multilayer perceptron make it a
popular choice for modeling nonlinear systems and for implementing
general-purpose nonlinear controllers This chapter introduces threepopular neural network architectures for prediction and control that
have been implemented in the Neural Network Toolbox software
Model Predictive Control
NARMA-L2 (or Feedback Linearization) Control
Model Reference Control
This chapter presents brief descriptions of each of these
architectures and demonstrates how you can use them There are
typically two steps involved when using neural networks for control
1 System identification
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2736
27
2 Control design
In the system identification stage you develop a neural network
model of the plant that you want to control In the control design stage
you use the neural network plant model to design (or train) the
controller In each of the three control architectures described in this
chapter the system identification stage is identical The control design
stage however is different for each architecture
bull For model predictive control the plant model is used to predict futur
behavior of the plant and an optimization algorithm is used to select th
control input that optimizes future performance
bull For NARMA-L2 control the controller is simply a rearrangement of th
plant model
bull For model reference control the controller is a neural network that
trained to control a plant so that it follows a reference model The neura
network plant model is used to assist in the controller training
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2836
28
Controllers in NNET Toolbox
Model Predictive Control mdash
This controller uses a neural network model to predict future plant
responses to potential control signals An optimization algorithm then
computes the control signals that optimize future plant performance
The neural network plant model is trained offline in batch form using
any of the training algorithms (This is true for all three control
architectures) The controller however requires a significant amount
of online computation because an optimization algorithm is performed
at each sample time to compute the optimal control input
NARMA-L2 Control mdash
This controller requires the least computation of these three
architectures The controller is simply a rearrangement of the neural
network plant model which is trained offline in batch form The only
online computation is a forward pass through the neural network
controller The drawback of this method is that the plant must either be
in companion form or be capable of approximation by a companion
form model
Model Reference Control mdash
The online computation of this controller like NARMA-L2 is
minimal However unlike NARMA-L2 the model reference architecturerequires that a separate neural network controller be trained offline in
addition to the neural network plant model The controller training is
computationally expensive because it requires the use of dynamic back
propagation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2936
29
Fig13 Neural Network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3036
30
Fig14 Testing Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2636
26
mechanism When we change a specific operating condition meaning
the set point we would like for example the pH of the bioreactor to
follow our command
This is what we call servo control The pH value of the bioreactor
is subjected to external disturbances (also called load changes) and the
task of suppressing or rejecting the effects of disturbances is called
regulatory control Implementation of a controller may lead to
instability and the issue of system stability is a major concern The
control system also has to be robust such that it is not overly sensitive
to changes in process parameters
Neural Network in Control Systems
Neural networks have been applied successfully in the
identification and control of dynamic systems The universal
approximation capabilities of the multilayer perceptron make it a
popular choice for modeling nonlinear systems and for implementing
general-purpose nonlinear controllers This chapter introduces threepopular neural network architectures for prediction and control that
have been implemented in the Neural Network Toolbox software
Model Predictive Control
NARMA-L2 (or Feedback Linearization) Control
Model Reference Control
This chapter presents brief descriptions of each of these
architectures and demonstrates how you can use them There are
typically two steps involved when using neural networks for control
1 System identification
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2736
27
2 Control design
In the system identification stage you develop a neural network
model of the plant that you want to control In the control design stage
you use the neural network plant model to design (or train) the
controller In each of the three control architectures described in this
chapter the system identification stage is identical The control design
stage however is different for each architecture
bull For model predictive control the plant model is used to predict futur
behavior of the plant and an optimization algorithm is used to select th
control input that optimizes future performance
bull For NARMA-L2 control the controller is simply a rearrangement of th
plant model
bull For model reference control the controller is a neural network that
trained to control a plant so that it follows a reference model The neura
network plant model is used to assist in the controller training
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2836
28
Controllers in NNET Toolbox
Model Predictive Control mdash
This controller uses a neural network model to predict future plant
responses to potential control signals An optimization algorithm then
computes the control signals that optimize future plant performance
The neural network plant model is trained offline in batch form using
any of the training algorithms (This is true for all three control
architectures) The controller however requires a significant amount
of online computation because an optimization algorithm is performed
at each sample time to compute the optimal control input
NARMA-L2 Control mdash
This controller requires the least computation of these three
architectures The controller is simply a rearrangement of the neural
network plant model which is trained offline in batch form The only
online computation is a forward pass through the neural network
controller The drawback of this method is that the plant must either be
in companion form or be capable of approximation by a companion
form model
Model Reference Control mdash
The online computation of this controller like NARMA-L2 is
minimal However unlike NARMA-L2 the model reference architecturerequires that a separate neural network controller be trained offline in
addition to the neural network plant model The controller training is
computationally expensive because it requires the use of dynamic back
propagation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2936
29
Fig13 Neural Network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3036
30
Fig14 Testing Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2736
27
2 Control design
In the system identification stage you develop a neural network
model of the plant that you want to control In the control design stage
you use the neural network plant model to design (or train) the
controller In each of the three control architectures described in this
chapter the system identification stage is identical The control design
stage however is different for each architecture
bull For model predictive control the plant model is used to predict futur
behavior of the plant and an optimization algorithm is used to select th
control input that optimizes future performance
bull For NARMA-L2 control the controller is simply a rearrangement of th
plant model
bull For model reference control the controller is a neural network that
trained to control a plant so that it follows a reference model The neura
network plant model is used to assist in the controller training
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2836
28
Controllers in NNET Toolbox
Model Predictive Control mdash
This controller uses a neural network model to predict future plant
responses to potential control signals An optimization algorithm then
computes the control signals that optimize future plant performance
The neural network plant model is trained offline in batch form using
any of the training algorithms (This is true for all three control
architectures) The controller however requires a significant amount
of online computation because an optimization algorithm is performed
at each sample time to compute the optimal control input
NARMA-L2 Control mdash
This controller requires the least computation of these three
architectures The controller is simply a rearrangement of the neural
network plant model which is trained offline in batch form The only
online computation is a forward pass through the neural network
controller The drawback of this method is that the plant must either be
in companion form or be capable of approximation by a companion
form model
Model Reference Control mdash
The online computation of this controller like NARMA-L2 is
minimal However unlike NARMA-L2 the model reference architecturerequires that a separate neural network controller be trained offline in
addition to the neural network plant model The controller training is
computationally expensive because it requires the use of dynamic back
propagation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2936
29
Fig13 Neural Network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3036
30
Fig14 Testing Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2836
28
Controllers in NNET Toolbox
Model Predictive Control mdash
This controller uses a neural network model to predict future plant
responses to potential control signals An optimization algorithm then
computes the control signals that optimize future plant performance
The neural network plant model is trained offline in batch form using
any of the training algorithms (This is true for all three control
architectures) The controller however requires a significant amount
of online computation because an optimization algorithm is performed
at each sample time to compute the optimal control input
NARMA-L2 Control mdash
This controller requires the least computation of these three
architectures The controller is simply a rearrangement of the neural
network plant model which is trained offline in batch form The only
online computation is a forward pass through the neural network
controller The drawback of this method is that the plant must either be
in companion form or be capable of approximation by a companion
form model
Model Reference Control mdash
The online computation of this controller like NARMA-L2 is
minimal However unlike NARMA-L2 the model reference architecturerequires that a separate neural network controller be trained offline in
addition to the neural network plant model The controller training is
computationally expensive because it requires the use of dynamic back
propagation
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2936
29
Fig13 Neural Network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3036
30
Fig14 Testing Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 2936
29
Fig13 Neural Network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3036
30
Fig14 Testing Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3036
30
Fig14 Testing Data
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3136
31
Fig 15 Training Behaviour
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3236
32
Fig16 Neural Model and its Paramaters
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3336
33
Fig17 Simulation 1
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3436
34
Fig18 Simulation 2
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3536
35
CONCLUSION
The present report represents a simulation programs in MATLAB
language used to study and develop a mathematical model of the
dynamic behavior of neutralization process in a continuous stirred tank
heater (CSTH) and the process control implemented using different
control strategies The following conclusions can be drawn
1 For now the NARMA-L2 controller of NNET toolbox is very fast
relative to the NN predictive model which takes a longer time even in
the simulation
2 NN predictive model is more accurate for the data training that we
have used
3 Volume plays a big role in the control strategy as the increase in
volume decreases the sensitivity of the model hence the NN predictive
model gets more accurate
4 Training for 1000 data sets is enough to train the neural network
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem
5172018 ANN Based pH Control Report - slidepdfcom
httpslidepdfcomreaderfullann-based-ph-control-report 3636
36
REFERENCES
Process Control by Prof Surekha Bhanot
What are Artificial Neural Networks by Anders Krogh
wwwmathworkscom
Modified Functional Link Artificial Neural Network by Ashok Kumar
Goel Suresh Chandra Saxena and Surekha Bhanot
Neuro modeling and control strategies for a pH process by
ESivaraman and SArulselvi
Adaptive control of a pH Process by DrKarima M Putrus and
Zahraa F Zihwar
Modified Mathematical Model For Neutralization System In Stirred
Tank Reactor by Ahmmed Saadi Ibrehem