fuzzy based pid for calciner temperature …fuzzy pid were shown in figures 6.3 and 6 .4 re...

Abstract— The Calciner Unit plays an important role in the

modern cement industries as it is used for preheating the raw

materials like limestone which are fed into the kiln. The

mathematical model of the calciner unit is designed using

System Identification technique for the real time data obtained

from the plant. A conventional PID controller has been

designed to control the temperature of the calciner unit. The

parameter of PID controller is tuned using Ziegler – Nichols

tuning method. In order to achieve optimum controller

parameter a Self Tuning Fuzzy PID controller is developed. The

performance of the calciner unit has improved significantly

compared to conventional PID controller.

I. INTRODUCTION

Calciner temperature control process is one of the

most important processes in cement manufacturing. It is used

to maintain the raw mix texture, size of the mixture and

perfect blending of the raw material to produce more

valuable clinker. Calciner unit is used to preheat the raw mix

sent into the kiln. The product obtained is “clinker”

(cement). Normal temperature of kiln is to be maintained at

800-960 °C and a normal coal feeding is 10-20 t/hr. There

are four basic processes in cement manufacturing. It starts

with quarry where the raw material is extracted and crushed.

Then it will be sent to raw mill wherein the blending process

takes place (raw mix). The resultant from the above process

was sent to the calciner where the raw mix was preheated

and fed into the kiln. The raw mix and fuel was sent into the

kiln. Clinker and exit gases come out. The clinker was sent

to finish mill, after which the size was reduced to obtain the

final product „cement„. The basic schematic diagram of

cement manufacturing plant is shown in Fig.1.1.

Figure 1.1: Schematic diagram of cement manufacturing

plant

II. IDENTIFICATION OF SYSTEM

A. ANALYZING AND PROCESSING DATA

When preparing data for identifying models, it was

mandatory to specify information such as input-output

channel names, sampling time (10s). The toolbox helps to

attach this information to the data, which facilitates

visualization of data, domain conversion, and various

preprocessing tasks. Measured data often has offsets, slow

drifts, outliers, missing values, and other anomalies. The

toolbox removes such anomalies by performing operations

such as de-trending, filtering, resampling, and reconstruction

of missing data. The toolbox can analyze the suitability of

data for identification and provide diagnostics on the

persistence of excitation, existence of feedback loops, and

presence of nonlinearities. The toolbox estimates the impulse

and frequency responses of the system directly from

measured data. Using these responses, system characteristics,

such as dominant time constants, input delays, and resonant

frequencies can be analyzed. These characteristics can also

be used to configure the parametric models during

estimation.

FUZZY BASED PID FOR CALCINER TEMPERATURE

CONTROL

Mrs.Z.Brijet *1

, M.B.Sri Padmadarshan*2

, S.Vigneshwaran*3

, P.B.Mohankrishna*4

*1 Assistant Professor – III, Department of Electronics and Instrumentation Engineering, Velammal

Engineering College, „Velammal New-Gen Park, Ambattur-Red Hills Road, Chennai – 600066,

India *[email protected]

*2,3,4 4th year Bachelor‟s degree, Department of Electronics and Instrumentation Engineering,

Velammal Engineering College, „Velammal New-Gen Park, Ambattur-Red Hills Road, Chennai –

600066, India *2

[email protected],*3

[email protected],*4

[email protected]

International Journal of Pure and Applied MathematicsVolume 119 No. 12 2018, 14563-14570ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu

14563

B. ESTIMATING MODEL PARAMETERS

Parametric models, such as transfer functions or state-

space models use a small number of parameters to capture

system dynamics. System Identification Toolbox estimates

model parameters and their uncertainties from time-response

and frequency-response data. These models can be analyzed

using time-response and frequency-response plots, such as

step, impulse, bode plots, and pole-zero maps.

C. VALIDATING RESULTS

System Identification Toolbox helps to validate the

accuracy of identified models using independent sets of

measured data from a real system. For a given set of input

data, the toolbox computes the output of the identified model

and lets to compare that output with the measured output

from a real system. One can also view the prediction error

and produce time-response and frequency-response plots

with confidence bounds to visualize the effect of parameter

uncertainties on model responses.

Figure 2.1: Shows the process of selecting the range for

validation and estimation of data.

D. LINEAR MODEL IDENTIFICATION

System Identification Toolbox lets to estimate

multi-input, multi-output continuous or discrete-time transfer

functions with a specified number of poles and zeros. One

can specify the transport delay or let the toolbox determine it

automatically. In this work, transfer function model was used

for system identification.

E. ESTIMATING TRANSFER FUNCTION MODEL

Estimate continuous-time and discrete-time transfer

functions and low-order process models. Use the estimate

models for analysis and control design. Polynomial and

state-space models can be identified using estimation

routines provided in the toolbox. These routines include

autoregressive models (ARX, ARMAX), Box-Jenkins

models, Output-Error models, and state-space

parameterizations. Estimation techniques include maximum

likelihood, prediction-error minimization schemes, and

subspace methods based on N4SID, CVA, and MOESP

algorithms. A model of the noise affecting the observed

system can also be estimated. Figure 2.2 depicts the process

of obtaining the transfer function model.

Figure 2.2: Obtaining transfer function model

F. ESTIMATING STATE-SPACE MODEL

A state space model is commonly used for

representing a linear time invariant system. It describes a

system with a set of first order difference equation using

inputs, outputs and state variables. In the absence of the

equation, a model of desired order can be estimated for

measured input, output data. The model was widely used in

modern control application for designing controllers and

analyzing system performance in the time domain and

frequency domain. The models can be applied to nonlinear

system or system with a non-zero initial condition.

Figure 2.3: Obtaining state space model

International Journal of Pure and Applied Mathematics Special Issue

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III. DESIGN OF PID CONTROLLER FOR

CALCINER

A. PID CONTROLLER:

P-I-D controller has the optimum control dynamics

including steady state error, fast response, less oscillations

and higher stability. The necessity of using a derivative gain

component in addition to the P-I-D controller is to eliminate

the overshoot and the oscillations occurring in the output

response of the system. One of the main advantages of the P-

I-D controller was that it can be used with higher order

processes including more than single energy storage.

From a mathematical viewpoint, the PID control works to

reduce the error e(t) to zero, where e(t) was the difference

between output response and the set point.

The control response u(t) is given by:

u(t)=Kpe(t)+Ki∫e(t)dt+Kd de(t)/dt

where kp, ki, kd are scale factors for the proportional,

integral and differential terms respectively.

B. ZIEGLER – NICHOLS TUNING METHOD:

The basic steps in Z-M method are

1. The value of Kd and Ki were set to zero.

2. The value of Kp was slowly increased such the sustained

oscillation occurs (constant amplitude and periodic).

3. The value of Kp at which sustained oscillation occurs was

ultimate gain Ku and the period of oscillation was ultimate

period Pu.

From the calculated value of Ku and Pu, the parameters of

PID controller were calculated using the formula:

The table 3.1 shows the PID controller parameter tuned

using Ziegler – Nichols method.

Table 3.1: PID controller tuning parameters

Control type Kp Ki Kd

PID 0.6*200=120 2/0.2=10 0.2/8=0.025

IV. DESIGN OF FUZZY CONTROLLER

Figure 4.1: General block diagram of fuzzy logic controller

A. FUZZY INFERENCE SYSTEM

A Fuzzy inference system (FIS) was a system that

uses fuzzy set theory to map inputs to outputs. There are two

types of FIS .They are mamdani and Takagi sugeno FIS. In

this project there are two inputs and three outputs. Therefore,

mamdani type FIS was used in this project.

i. MAMDANI FIS

Mamdani FIS is widely accepted since it

can be applied for both MIMO, MISO systems whereas

sugeno can be implemented only for MISO systems. In

mamdani, the membership functions can be chosen even for

outputs whereas it was not possible in sugeno type. Hence

mamdani FIS was used for our project.

ii. DEVELOPMENT OF MAMDANI TYPE FIS

Calciner temperature in the cement

manufacturing process was developed using mamdani fuzzy

model. It consists of two inputs and three outputs. First input

was error. Second input was rate of change of error. The

three outputs were Kp, Ki and Kd (i.e. controller gains).

Table 4.1:Rule table of fuzzy controller

B. MAMDANI FIS IMPLEMENTATION FOR

CALCINER TEMPERATURE CONTROL

Figure 4.2: Fuzzy logic toolbox


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Figure 4.3: Membership function of inputs

Figure 4.4: Membership function of outputs

Figure 4.5: Rule viewer of mamdani FIS

Figure 4.6: Surface viewer of mamdani FIS

V. IMPLEMENTATION OF FUZZY PID

CONTROLLER

A. STRUCTURE OF FUZZY-PID CONTROLLER

Self tuning fuzzy-PID controller means that the three

parameters Kp, Ki, and Kd of PID controller are tuned by

using fuzzy tuner. The coefficients of the conventional PID

controller are not often properly tuned for the non-linear

plant with unpredictable parameter variations .Hence, it was

necessary to automatically tune the PID parameters.

Figure 5.1: Structure of the self tuning fuzzy-PID controller

The error and the derivative of its error are sent to the fuzzy

controller. The PID parameter Kp, Ki and Kd is calculated

according to the rules in the fuzzy controller, at the same

time, Kp was also refined by P controller which was the

immune PID controller, so the Kp, Ki and Kd can be

continuous updated according to error e(t) and its derivative

de/dt.

VI. SIMULATION RESULTS AND DISCUSSION

A. SERVO RESPONSE OF PID AND FUZZY PID

CONTROLLER

Simulation studies are carried out to demonstrate the

tracking capability of tuned PID controller and fuzzy PID

controller. The performance of process for tuned PID and


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fuzzy PID were shown in figures 6.3 and 6.4 respectively.

From the response, it was observed that the calciner

temperature follow the given set points and the servo

response of the PID and fuzzy PID were compared in the

table 8.1.

Fig 6.1: Servo response of the PID controller

Fig 6.2: Servo response of the fuzzy PID controller

Table 6.1: Comparison of performance indices of PID and

FUZZY PID tuned controller for servo response

CALCINER

TEMPERATURE

CONTROL USING

ISE IAE ITAE

PID CONTROLLER 1.559

e^(+05)

416.9 3975

FUZZY CONTROLLER 1.045

e^(+05)

279.3 2138

From the responses, it was observed that the performance

criterion such as ISE, IAE and ITAE of Fuzzy PID controller

was better compared to conventional PID controller. It was

also observed that fuzzy PID controller settles quickly than

PID controller response.

B. SERVO WITH REGULATORY RESPONSE OF PID

AND FUZZY PID CONTROLLER

Fig 6.5: Servo with regulatory response of the PID controller

Fig 6.6: Servo with regulatory response of the fuzzy PID

controller

Table 6.2: Comparison of performance indices of PID and

FUZZY PID controller for servo with regulatory response

CALCINER

TEMPERATURE

CONTROL USING

ISE IAE ITAE

PID CONTROLLER 1.605e^(+05) 622.8 9293

FUZZY CONTROLLER 1.294 e^(+05) 410.9 4294

VII. REAL TIME IMPLEMENTATION –

CEMULATOR

Contrary to most cement process simulators,

ECS/CEMULATOR was developed on a full functional

control systems platform enabling the complete set of

functions and features of a modern control system

environment for the users. Having a skilled team of operators

plays a crucial role in beneficial and safe operation of

industrial plants. Especially in the cement industry, with the

significant high cost of investment, practical knowledge and

experience of plant operation have a direct effect on

production economy. Insufficient insight in process


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dynamics and interactions, high stress factors in real time

operation conditions, and lack of adequate experience in

utilizing the existing control system are typical reasons for

incorrect operator actions. The consequences of this may

result in low production quality, production interrupts, and

equipment damage, in worst case risk on human safety. The

increasing demand on production sustainability in the recent

years has resulted in requirements of which the degree of

fulfillment is effected by the level of skills of plant operators

and engineers.

A. REAL TIME RESPONSE OF THE PID

CONTROLLER

Figure 7.1: Response of PID controller in real time

B. REAL TIME RESPONSE OF FUZZY PID

CONTROLLER

Figure 7.2: Response of Fuzzy PID controller in real time

Comparison of performance indices of PID and FUZZY PID

controller for the real time response is shown in Fig. 7.1 and

7.2.

Table 7.1:

CALCINER TEMPERATURE CONTROL

USING

ISE

PID CONTROLLER

18.4

FUZZY CONTROLLER 16.4

From the table 7.1 it has been observed that Integral Square

Error (ISE) value of fuzzy PID controller is reduced as

compared to PID controller.

VIII. CONCLUSION

The main aim of the project was to control the

calciner temperature and to obtain a good quality clinker.

The transfer function model of calciner for the process has

been derived using system identification tool. The simulink

model of calciner has been developed in MATLAB using

real time steady state values of Turkey power plant. The

open loop response of the process where observed and the

interaction effect has been studied. The parameters for PID

were obtained using Ziegler - Nichols tuning. The fuzzy

rules were written using FAM table and the rules are inserted

in the FIS using mamdani method which is used to tune the

PID. Thus Fuzzy PID controller was implemented and then

optimized values were obtained. It is observed that the

performance criteria namely the ISE, IAE, ITAE, and

settling time in Fuzzy PID controller is better than the PID

controller. Also from the responses, it has been observed that

the proposed method has better tracking and faster settling

time.

IX. APPENDIX

DATA FROM REAL TIME CALCINER UNIT

S.N

O

CALCINER

TEMPERATU

RE

CALCINE

R COAL

FEED

KILN

TOTAL

FEED

1 894.7916 9.6501 588.4775

2 894.7916 9.6401 589.4781

3 896.5278 9.6359 585.4742

4 898.9583 9.6276 588.4867

5 901.3889 9.6184 594.3333

6 904.1666 9.6096 590.6599

7 902.7778 9.6029 588.5881

8 900.6944 9.6033 590.9871

9 899.3055 9.6079 591.7212

10 901.3889 9.6074 589.3926

11 903.1249 9.6 585.8295

12 901.7361 9.5952 584.7019

13 900.6944 9.5972 586.1656

14 901.0416 9.5997 590.9084

15 903.1249 9.5979 590.3184

16 906.2499 9.5892 591.2415


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17 904.8611 9.5817 590.2633

18 903.1249 9.5822 591.3748

19 902.7778 9.5847 591.8418

20 906.9444 9.5828 585.3685

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fuzzy based pid for calciner temperature …fuzzy pid were shown in figures 6.3 and 6 .4 re...

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