Design, Implementation, and Simulation of Control Systems for Extractive and Recovery Distillation Columns using
Aspen Plus and Aspen Dynamics
Thesis Report ENG470 – Engineering Honours Thesis
Ashen Sheranga Jayasinghe
24.08.2018
Unit Co-ordinator: Dr. Gareth Lee
Thesis Supervisor: Dr. Linh Vu
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I, Ashen Sheranga Jayasinghe, submit this document to School of Engineering and Energy completing the requirements of the undergraduate course at Murdoch University. I with this declare that this thesis document is my own work except the idea of the project which is referenced. Furthermore, this document has not been submitted to any other school or academic institution.
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Ashen S Jayasinghe 24.08.2018
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Abstract
In practice, distillation may be carried out by either of two methods. The first method is based
on the production of vapor by boiling the liquid mixture to be separated and condensing the
vapour without allowing any liquid to return to the still. Then, there is no reflux. The second
method is based upon the return of part of the condensate to the still under such conditions
that this returning liquid is brought into the intimate contact with the vapours on their way to
the condenser. Either of these two methods may be conducted as a continuous or as a batch
process, but the study of dynamics and control of the process is one of most important part of
each process.
Distillation is one of the commonly used separation technique in the chemical industries. The
separation is based on differences in “volatilities” (tendencies to vaporize) among various
chemical components. In a distillation column the more volatile, or lighter, components are
removed from the top of the column, and the less volatile, or heavier, components are
removed from the lower part of the column. Further Aspen Plus makes it easy to build and
run the process simulation model by providing with a comprehensive system of the online
process modelling. Process simulation allows one to predict the behaviour of a process by
using basic engineering relationships, such as mass and energy balances, and phase and
chemical equilibrium. Process simulation enables one to run many cases, conduct „what if‟
analysis and perform sensitivity analysis and optimization runs. With simulation one can
design better plants and increase the profitability of the existing plants. Process simulation is
helpful throughout the entire life of a process, from research and development through
process design to production.
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This thesis studies the dynamics and control of distillation columns using Aspen Plus. In this
thesis, simulation studies of the distillation column are presented. Steady-state simulations
are being performed using Aspen Plus followed by Aspen Dynamic simulation. In the steady
state simulation, it was tried to see the effect of changing the flow rate of the extractive
distillation. And finding the optimum flow rate in the distillation column. Controllers are then
implemented for controlling sump level, reflux level and feed flow rate. Furthermore, two
strategies were used for controlling the purity of distillate product controlling the distillation
column tray temperature where the maximum change of temperature is observed due to
reboiler heat change and the purity of the product by using composition controller.
The case study was an example taken from Aspen Plus (version 8.4v). In the example, there
are two main streams enters the distillation column and phenol will be the stream one, and
methyl cyclone hexane (MCH) and toluene mixer will enter the distillation column as the
second stream. MCH has been distilled from the top of the column and the phenol and
toluene the bottom product. With the latest Aspen Plus and Aspen Dynamics version V10
with operating under Windows 10, because of that, we will come across few compatibility
issues in Aspen Dynamics mainly when it comes to MATLAB. Moreover, due to
incompatibility MATLAB and Simulink were not tested for this process.
In this study, Methyl Cyclo Hexane (MCH) been separated from Toluene by using Phenol as
the third component in an extractive distillation column. And in Aspen Dynamics new
controllers been developed to control the product Methyl Cyclo Hexane (MCH) purity by
making adjusting the flow rate level of the Phenol. DMC controllers were tried to implement
in the process to replaces the PI controller but fail attempt.
All the PI controllers have been auto-tuned in Aspen Dynamics using it tool of the faceplates.
Which given the best possible controller parameters to for the process. Therefore, all the
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controller’s other was able to reach its set-point expect the composition controller. The
controllers were helping to achieve the maximum purity of the distillate stream.
All the obtained results have been discussed and the Important guidelines been outlined and
explained in the overall simulation. Most of the objective been achieved in this thesis.
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Acknowledgments
Foremost, I would like to express my sincere gratitude to my advisor Dr Linh Vu for her continuous support of my thesis, for her patience, motivation, enthusiasm, and immense knowledge. Her guidance helped me in all the time of research and writing of this thesis. I could not have imagined having a better advisor and mentor for my thesis.
Beside my advisor, I would like to thank Dr Gareth Lee, the unit coordinator, for his encouragement, insightful comments, being patient with me through this thesis project.
I thank my fellow friends: Heshan Jayasinghe, Ornela Munaweera, Vohar Siriwardana, Sheshan kankanamge for their help with the report writing. For all the sleepless nights we were working together before deadlines.
Last but not the least, I would like to thank my family: Kanth Jayasinghe and Sherren Jayasinghe for being my backbone throughout my journey in completing this project.
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Glossary
ICE - Instrumentation and Control Engineering
MCH - Methyl Cyclo Hexane
DISTL - Extractive Distillation Column
WDC - Wall distillation column
TOL - Toluene
ATV - Auto tune variation
τD - Derivative time constant
τi - Integral time constant
Kc - Gain
Pu - Ultimate period of oscillation
Ku - Ultimate controller gain
k - Bias value
K - Kelvin
F - Fahrenheit
W - watts
psia - Pound-force per square inch
P11 & P12 - Pumps
V1 &V2 - Flow valves
V11 & V12 - Output valves
MV - Manipulated variable
PV - Process variable
OP - Operating point
SP - Set-point
PC - Pressure controller
SQP - Sequential quadratic programming
TC - Tray temperature controller
LC11 & LC12 - Level controllers
FC1 & FC2 - Flow level controllers
DMC - Dynamic Matrix Control
MPC - Model Predictive Control
RGA - Relative gain analysis
ACM - Aspen custom modeler
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Contents Abstract .................................................................................................................................................................. ii
Acknowledgments .................................................................................................................................................. v
Glossary ................................................................................................................................................................. vi
1. Introduction and Layout of the Project ......................................................................................................... 1
2. Background, Scope, and Aim of the Project ....................................................................................................... 3
2.1 Overview ........................................................................................................................................................... 3
2.2 Project Scope .................................................................................................................................................... 6
2.3 Project Aims ...................................................................................................................................................... 7
3. Literature Review ........................................................................................................................................... 8
3.1 Extractive Distillation ...................................................................................................................................... 10
3.1.1 Types of extractive distillation ..................................................................................................................... 11
3.2 Choice of Solvent ............................................................................................................................................ 11
3.3 Simulation ....................................................................................................................................................... 12
3.4 Steady-State Simulation ................................................................................................................................. 12
3.5 ASPEN DYNAMICS SIMULATION ..................................................................................................................... 13
3.5.1 TRAY TEMPERATURE CONTROLLER ............................................................................................................. 13
3.5.2 COMPOSITION CONTROLLER ....................................................................................................................... 16
4. Software Overview ...................................................................................................................................... 18
4.1 Aspen Plus....................................................................................................................................................... 18
5 Process modelling and simulation in Aspen Plus ............................................................................................... 20
5.1.1 Component Selection .................................................................................................................................. 21
5.1.2 Selection of Distillation Column .................................................................................................................. 23
5.1.3 Valves and Pumps ........................................................................................................................................ 26
5.1.4 Steady State Simulation ............................................................................................................................... 27
5.1.5 Sensitivity Analysis ....................................................................................................................................... 27
5.1.6 Design Specification ..................................................................................................................................... 30
5.1.7 Column Sizing .............................................................................................................................................. 30
5.1.8 Optimization ................................................................................................................................................ 32
5.1.9 Extractive Distillation ................................................................................................................................... 35
6. Dynamic and process control in Aspen Dynamics ............................................................................................ 36
6.1 Dynamic Simulation ........................................................................................................................................ 37
6.1.1 Basic Level Controlles .................................................................................................................................. 39
6.1.2 Basic level Controller Tuning ....................................................................................................................... 43
6.1.3 Tray temperature control ............................................................................................................................ 45
6.1.4 Relay- Feedback Test ................................................................................................................................... 47
6.1.5 Composition Controller ............................................................................................................................... 50
6.1.6 Set Point Change .......................................................................................................................................... 54
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6.1.7 Disturbance Change ..................................................................................................................................... 54
7.0 Results and Discussion .................................................................................................................................... 55
7.1 Installing temperature and composition controller ....................................................................................... 55
7.2 DMC Design Parameter .................................................................................................................................. 56
8. Conclusion .................................................................................................................................................... 57
9. Future Work ................................................................................................................................................. 59
9.1 Review the Composition Controller and the Dynamic Matrix Control (DMC) From the results obtained in Section .................................................................................................................................................................. 59
9.2 Implementing the Solvent Recovery Column ................................................................................................. 59
9.3 Relative Gain Analysis (RGA) ................................................................................................................ 60
9.4 Aspen Custom Modeler (ACM) .............................................................................................................. 61
10. Bibliography .................................................................................................................................................... 62
11. Work Cited ...................................................................................................................................................... 63
12 Appendices ...................................................................................................................................................... 65
12.1 Appendix A – Aspen Plus & Aspen Dynamic Results .................................................................................... 65
Figure 1 Extractive distillation and solvent recovery column ................................................................................. 5 Figure 2 Configuration of extractive distillation ................................................................................................... 10 Figure 3 Distillation column with tray temperature controller ............................................................................ 14 Figure 4 Distillate purity versus time, when feed flow rate is increased by 10% of its initial value for differ temperature set POINTS [22] ............................................................................................................................... 15 Figure 5 Variation OF PURITY and temperature when required distillate purity is 99% [22] ............................... 15 Figure 6 Distillation column with composition controller [19] ............................................................................. 16 Figure 7 Variation of the purity of the distillate for the composition controller when 99 molar percent of purity in the distillate is set [19] ..................................................................................................................................... 16 Figure 8 Distillate purity for the composition controller when the rate of molar feed flow is raised by 10% of its first value [19] ....................................................................................................................................................... 17 Figure 9 basic flowsheet of the process ............................................................................................................... 21 Figure 10 COMPONENT SELECTION of the process .............................................................................................. 22 Figure 11 DISTILLATION COLUMN CONFIGURATION ............................................................................................ 25 Figure 12 Specification of sensitivity analysis ....................................................................................................... 28 Figure 13 Sensitivity Result Curve......................................................................................................................... 29 Figure 14 Column sizing specification ................................................................................................................... 31 Figure 15 Dynamic pressure check ....................................................................................................................... 38 Figure 16 LC11 faceplate ...................................................................................................................................... 40 Figure 17 pressure controller faceplate ............................................................................................................... 41 Figure 18 Flowsheet of the basic controller ......................................................................................................... 42 Figure 19 MCH Test Tuning controller .................................................................................................................. 44 Figure 20 Tuning Calculation ................................................................................................................................ 44 Figure 21 Temperature controller faceplate ........................................................................................................ 46 Figure 22 Deadtime table ..................................................................................................................................... 46 Figure 23 Flowsheet with the temperature controller ......................................................................................... 47 Figure 24 Relay-feedback test results ................................................................................................................... 48 Figure 25 Calculated test results .......................................................................................................................... 49 Figure 26 Tuned Temperature controller ............................................................................................................. 50 Figure 27 Composition Controller faceplate ......................................................................................................... 51
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Figure 28 composition tuned controller ............................................................................................................... 52 Figure 29 Flowsheet of the composition controller with the dead time [∆T] ...................................................... 53 Figure 30 Tuned Composition controller .............................................................................................................. 53 Figure 31 steady-state results .............................................................................................................................. 66 Figure 32 TEMPERATURE OF INDIVIDUAL 22 STAGES .......................................................................................... 68 Figure 33 Pressure Level of individual stages ....................................................................................................... 68 Figure 34 Hydraulic Plot ........................................................................................................................................ 69 Figure 35 tuned Basic level controller .................................................................................................................. 71
Table 1 Phenol & MCH-TOL Streams specification ............................................................................................... 23 Table 2 Steady State result ................................................................................................................................... 27 Table 3 Design Specification ................................................................................................................................ 30 Table 4 Optimization variable definition .............................................................................................................. 34 Table 5 optimization variable specification .......................................................................................................... 34 Table 6 Controller placement ............................................................................................................................... 39 Table 7 Controlled parameters ............................................................................................................................. 45 Table 8 Feed Stream ............................................................................................................................................. 65 Table 9 Product Stream ........................................................................................................................................ 65 Table 10 Steady-state results ............................................................................................................................... 66 Table 11 Temperature and Pressure at Steady state (all 22 stages) .................................................................... 67 Table 12 Sensitivity Analysis Results ..................................................................................................................... 69 Table 13 Column Internal Summary ..................................................................................................................... 69 Table 14 Selected Column Summary .................................................................................................................... 70 Table 15 optimization results ............................................................................................................................... 70
Equation 1 PID controller ..................................................................................................................................... 13 Equation 2 degrees of freedom ............................................................................................................................ 25 Equation 3 Gain Matrix (g) .................................................................................................................................... 61
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1. Introduction and Layout of the Project
Engineering learners at Murdoch University, particularly the students that are registered in
Instrumentation and Control Engineering (ICE) classes are introduced to different software
packages in their entire three years pursuing the course. In this course, the common software
utilized in the process control comprises Aspen One, MATLAB and toolboxes, in addition to
Lab VIEW.
The packages offered in ICE course have got both strengths and weaknesses during the
process modelling, as well as simulation. The central goal of the project is to integrate the
unique characteristics of every software package, for instance, co-simulating a distillation
column employing Aspen Plus, Aspen Dynamics, and MATLAB Simulink toolbox.
Consequently, to attain this goal, the subsequent tasks have been discussed in the entire
project, which is covered in this particular thesis:
Section 1: Introduction as well as layout of the project
Section 2: Background, scope, besides aim of the project
This section explains the extractive distillation column that is utilized in the case
study of this ICE project. Thus, the contrast of diverse software packages utilized in
modelling in addition to simulation is covered. Furthermore, the scope along with the
aim of this project is described in this section too.
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Section 3: The Process modeling and simulation in Aspen Plus
This section of the project describes the modelling of the extractive distillation
column in the Aspen Plus. The findings of the project simulations that are
fundamental for exporting the representation to Aspen Dynamics are covered too in
this section.
Section 4: Dynamic simulation besides process control in Aspen Dynamics
This section of the project describes the dynamic simulation that covers the extractive
distillation column in the Aspen Dynamics. In this section, the resultant system would
be experienced using the open loop in addition to closed loop. Also, the purity of the
distillate obtained is managed to attain the needed quality of the outcome that is
needed.
Section 5: Co-simulation of the extractive distillation column utilizing Aspen
Dynamics plus MATLAB Simulink toolbox
This section demonstrates the dynamic simulation that touches the extractive
distillation column together with co-simulating employing Aspen Dynamics, as well
as MATLAB Simulink toolbox, in which the procedure using a similar technique as in
section four.
Section 6: Results and discussion
Section 6 of the project explains and explores the findings of simulation gained from
the case study of this project.
Section 7: Conclusion
The section summarizes and makes the conclusion of the report.
Section 8: Future work
This section describes the future work recommended for prospect learners.
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2. Background, Scope, and Aim of the Project The project describes the co-simulation of the extractive distillation column utilizing Aspen
Plus Dynamics together with Simulink along with MATLAB. Thus, this part has all the
essential background, as well as related data regarding the simulation process; the section
contains an overview of the entire project, software, distillation column, as well as the
Solvent (entractant) utilized in the project simulations.
2.1 Overview The project will emphasize steady state, as well as the dynamic simulations of distillation
employing Aspen Plus, Aspen Dynamic, in addition to Simulink. The Aspen Plus is a steady
state simulation used to get modelling of the extractive column since the goal is to articulate
dynamic procedures based on the laws of conservation of mass along with energy.
Consequently, it is exceptionally useful particularly in understanding the nature of the
process and control design. The dynamic simulation was undertaken through Aspen
Dynamics that allows the understanding of the dynamics of the system of the project. Finally,
co-simulation the process of the distillation column is carried out employing Aspen
Dynamics along with Simulink.
Industry in general, extractive distillation process is a mix of some different procedures, in
which it can be split into two sections (shown in Figure 1); section one comprises the
extractive column, while section two is solvent recovery column. In the extractive column
(section 1), it is utilized to isolate the substance from the composition blend of the solvent
that is difficult to be isolated by ordinary distillation, forming a distillate product that
comprises the substances having the required purity. Thus, this is undertaken by employing a
third constituent (extractant) that will create an impact on the separation of the substances.
This constituent should be non-volatile, higher boiling point, as well as miscible with the
mix; however, it should not produce an azeotrope in the blend [1]. Additionally, the disparity
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in the interface of the 3rd constituent along with the mixture results in a transform in
comparative volatility, which permits the resultant combination of constituents plus solvent to
be estranged effectively. The constituent with the uppermost volatility will break up as the
major result as the top stream [2]. In the case of solvent recovery column (section 2), it is
utilized to separate the low volatility solvent from the extractive column, in which the
entrainer will be flowing incessantly.
In the case study, the primary constituents utilized comprise Methyl Cyclo Hexane (MCH),
Toluene, and Phenol. Accordingly, the primary feed flow is a blend of Methyl Cyclo Hexane
(MCH) as well as Toluene while the second feed flow is Phenol, which is the extractant. In
this instance, the combination of MCH plus Toluene will be separated through the use of
Phenol that functions as the third constituent. The Phenol is extracting Toluene from the
combination, where the two constituents settle at the base of the distillation line since they are
intense components of the mixture. In the meantime, MCH will be removed from the top of
the distillation column because it is the lighter element [1]. Accordingly, the combination of
Toluene plus Phenol after parting the extractive column is supplied to the solvent recovery
column in order to break up Toluene and Phenol [3]. While the mixture is in the solvent
recovery column, Toluene is extracted at the top while Phenol will be obtained from the
bottom stream that is then recycled then fed to the extraction column in the form the
entrainer. The mixture fundamentally charges the system with the quantity of the process
where this extrainer is re-circulated in the entire system. Therefore, this implies that in ideal
circumstances, there will be no extrainer that will be lost. Figure 1 illustrates the system of
the extractive column along with solvent recuperation column.
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FIGURE 1 EXTRACTIVE DISTILLATION AND SOLVENT RECOVERY COLUMN
In this project, it is apparent that only the extractive column is modelled, simulated, as well as
controlled. While the process of solvent recovery is reserved for upcoming students, the
system’s extractive column would be tested beginning with steady-state procedure till
dynamic procedure is attained. Aspen Plus, in this case, is utilized to carry out steady state
procedure while Aspen Dynamics plus Simulink are utilized to operate the dynamic
procedure. Furthermore, the dynamic procedure would be tested using the open and closed-
loop system, in which both software programs are utilizing similar parameters for controlling
the process. The findings of both software programs were contrasted to decide which
software that is better for controlling the procedure.
The distillation process is utilized to separate a blend into one or more distinct substances by
employing a heating source. Hence, the resulting outcome of the distillation process will have
the needed purity through controlling the boiler along with the condenser [4]. Moreover,
there are different kinds of advanced distillation methods out in the industry, like Vacuum,
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Cryogenic, Reactive, Extractive, as well as Pressure Swing [4]. The extractive distillation
process which been used in this case study to separate the process is difficult. Therefore, the
third component been feed into the process to separate the mixture.
2.2 Project Scope In this thesis project is designed to explore the co-simulation of a procedure of the distillation
column through utilizing Aspen Plus, Aspen Dynamics, in addition to MATLAB Simulink.
The project is only entailed with the extractive refinement column, the process modelling,
plus the steady-state simulation in Aspen Plus, bringing in the stable state representation from
Aspen Plus to Aspen Dynamics, as well as the designing plus testing the controllers in Aspen
Dynamics and MATLAB Simulink toolbox.
As a result, the primary goal of the project is to review a case, which was done in 2004
having newer software editions and an operating system (OS). Also, sensitivity analysis is
carried out in Aspen Plus to discover the mass flow-rate of the phenol that enters the
distillation column, where the purity of Methyl Cyclo Hexane (MCH) that leaves the
distillation column may attain around 0.98%.
The present controllers are refined in Aspen Dynamics to obtain the suitable parameters. This
will be followed by changes in set points that are undertaken in every controller while
disturbance changes are brought in based on particular variables to study the effect, as well as
response onto the purity of the product of the process. Lastly, a controller is designed to
control the flow-rate of phenol. A similar technique is utilized in co-simulation to test the
limpidness of the resultant substance. Nonetheless, using a non-identical column will not be
the scale of the project.
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2.3 Project Aims The prime aim of this case study is to experiment the co-simulation and relationship of
various software programs. Which was due to the errors in the relationship with Aspen
Dynamics version 10v and MATLAB Simulink form 9.3 by utilizing the Windows 10
operating system.
In this case, PI controllers been utilized in Aspen Dynamics and Simulink to control the feed
tank level, reboiler level, reflux drum level in addition to top stream pressure designed for an
extractive distillation and another controller in the phenol flowrate (entractant)
A particular set of goal been set in this project to improve the case study and its
understanding. Because of the advanced software entailed in the project, it will be paramount
to comprehend each software that will be used in the study.
The fields of examination comprise:
Understanding and familiarizing with Aspen Plus.
Investigating Aspen Dynamics
Mastering Simulink plus MATLAB.
he primary goal behind investigating the profundity is because of the constraint of
comprehension in utilizing these softwares. All through the further comprehension on the
actual interactions between the streams and on the product particularly how information is
sent, obtained, as well as retrieved.
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3. Literature Review
Rectifying section and stripping are two sections that make up a distillation column. Two
distillation columns are needed one Extractive column and one Solvent column for separation
of Methylcyclone (MCH) from Toluene. We need to add the solvent distillation column
because the MCH and toluene forms a close boiling mixture and therefore the conventional
method of distillation cannot be carried out to separate them. Hence, Phenol is used as a
solvent to separate them. Overall, four sections are needed to separate three component
mixtures [23]. Each column section, including rectifying section and stripping section
features a condenser and a reboiler. Obtaining output through little use of energy has been the
emerging trend within in the process. The demand is attained through the division of the wall
distillation column (WDC), which is an application in extractive distillation where two
columns been combined using a dividing wall. Three components are separated into two
columns instead of one column as performed in traditional technique. The major benefit of
WDC involves avoiding the problem of remixing, which was the issue within traditional
distillation techniques. Hence WDC could be considered an energy effective technique and a
substitute to the traditional one [22]. Within WDC, a wall separating the column from the
side stream zone and the product zone exists, and enhance WDC effectiveness as compared
to the traditional one [18]. Dividing wall within the column helps in purifying the product. To
have the highest purity, product insulation of the dividing wall is undertaken to prevent the
transfer of heat through it. Generally, the dividing wall is inserted in the middle; however, the
position could be changed based on the medium boiling components. This variation in the
placement of the wall is identified when the medium boiling component is negligible than the
bottom, and overhead product obtained [21]. Few assumptions were made before the
preparation of the WDC model. Notably, the pressure maintained within the column is
unchanged without the dynamic flow of vapour, energy balance, whereas enthalpy changes
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are ignored. Different control mechanisms were contrasted with this model [24]. The second
assumption focuses on suitable components where heat plus mass transfer occurs amid
vapour stages and its liquid [19].
In this review, the smallest boiling Isopropanol-water (an azeotrope) is alienated with an
entrainer DMSO through dividing wall distillation column (DWC) and traditional distillation
method. Aspen plus software offers numerous simulation models to allow maximum input
variables could be computed before the commencement of any process within a production
facility. In Aspen plus V10, divided wall distillation column is not given. Thus, a mimic
model has been used to obtain a high outcome that accurately replicates dividing wall
distillation based on functioning.
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3.1 Extractive Distillation Separation of azeotropic mixtures had posed numerous issues since, after explicit
concentrations, it creates steady boiling mixtures thus could not be purified further. This
challenge was tackled through extractive distillation. Thus, this technique utilized a 3rd
constituent that combines with one azeotropic constituent and escalates the volatility between
components of the azeotrope. The 3rd constituent is entrainer- a non-violent and miscible
solvent. A further volatile constituent is acquired as the top product whereas entrainer and
less unstable are acquired at the bottom [24]. Additional separation of the entrainer alongside
another constituent derived as the base product is attained through another supporting
column. Figure 2 below shows how the extractive distillation column been configured and
the valves and pumps been used to from the start to help with Aspen Dynamics.
FIGURE 2 CONFIGURATION OF EXTRACTIVE DISTILLATION
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3.1.1 Types of extractive distillation The process of extractive distillation could be placed into three crucial groups:
• For separation of binary mixtures to pure components
• For extrication low comparative volatility non-azeotropic combinations
• Separation of optimum boiling azeotropes
• Severance of minimum boiling azeotropes
Importantly, within extractive distillation, a solvent is intentionally included to expand the
boiling point variations of the comprising mixture species or to avert formation of azeotropes.
Thus, choice of proper solvent is usually considered crucial within the achievement of the
overall procedure.
3.2 Choice of Solvent A solvent to be utilized as the entrainer, rather, ought to have several characteristics that
include [9]:
• Non-toxic and non-corrosive
• Low latent heats
• Non-reactive with the components within the feed mixture
• Steady at the working temperature of the distillation column
• Readily available and inexpensive
• Easily separated from the bottom product
• Dissolves easily with feed components and do not create any two stages with it
• Ratio of solvent to non-solvent should be less
• comparative volatility of main constituents should be augmented
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3.3 Simulation In many instances, equations are used and required when designing distillation columns.
Thus, the component mass balance, variations in energy, gas and liquid equilibrium, along
with equations of hydraulic systems are fundamental towards the calculation of non-linear
equations regarding the distillation. These non-linear equations comprise multiple roots,
which implies that the outcome cannot meet since there will be several outcomes that will
originate from a similar set of inputs. Hence, the product of the simulation procedure shows
that there exists a rise in the reflux ratio.
This implies that the flow-rate of recycling declines when the fundamental number of trays
located in the dehydration column when a specific severance is attained, which shows a rise
in the heating process. The present distillation columns will be multifaceted when the
simulation process is proceeding. Accordingly, using Aspen-plus simulation method will play
a leading role in the convergence of the product of distillation.
3.4 Steady-State Simulation In this part, an extractive distillation column is used for demonstrating the manner to create a
steady-state simulation. Aspen V10 was utilized for simulating the model. The column design
is illustrated in Figure 2. Two feed streams that enter the distillation column exist: an MCH-
TOL (methycyclone and toluene mixture) is fed within the 14th phase and a solvent entrainer
stream (phenol), which is fed within the 7th phase. The column contains 22 phases that
include reboiler and the total condenser. Other working specifications are illustrated in
Figure 2.
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3.5 ASPEN DYNAMICS SIMULATION
After the completion of all steady-state simulations within the Aspen Plus, the relevant results
are obtained and tabulation graphs that show the behaviour of the system, which replicate the
inputs applied are created. Then such information is transferred to Aspen Dynamic. The
Aspen Dynamics window accompanies the closed loop process flow diagram alongside the
default pressure controller (PC). For the column to function properly, three more controllers
namely feed flow controller, base-level controller, and reflux drum level controller are
incorporated. After the setting up of essential controllers on the column has been completed,
two methods are employed to control the purity of the distillate they include composition
controller and tray temperature, controller. During the simulation, the ideal controller would
be the PID controller to control all the controllers [16] which is denoted below,
EQUATION 1 PID CONTROLLER
C=k +𝐶𝐶 = 𝑘𝑘 + 𝐾𝐾𝑐𝑐 �𝜀𝜀 + 1𝜏𝜏𝐼𝐼∫ 𝜀𝜀.𝑑𝑑𝑑𝑑 + 𝜏𝜏𝐷𝐷
𝑑𝑑(𝜀𝜀)𝑑𝑑𝑑𝑑�
Where C represents the output of the controller, k shows the bias value, 𝜏𝜏𝐷𝐷 , 𝜏𝜏𝐼𝐼 𝑎𝑎𝑎𝑎𝑑𝑑 𝐾𝐾𝐶𝐶
represent the derivative time constant, the integral time constant and the controller’s gains,
whereas ε =set point-process variable.
3.5.1 TRAY TEMPERATURE CONTROLLER
The tray temperature controller controls tray temperature. It is linked to the tray; this gives
optimum gain for change in temperature caused by small changes within the design variable.
Within the control action, the reflux ratio plays a crucial deciding factor. The placement of
the controller can be picked from steady-state simulation temperature results. Recommended
placement would be whichever two trays has the highest spike in temperature.
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FIGURE 3 DISTILLATION COLUMN WITH TRAY TEMPERATURE CONTROLLER [21]
Because Tyreus-Luyben offers more conventional conditions and as well further ideal for
chemical procedure control usages [21], it is utilized for tuning the tray temperature
controller. Thus, the PI controller’s tuned parameters can be used to obtain the parameters of
the controller.
After tuning and installing the temperature controller, the controller performance is tested for
regulatory and servo control method. Within the regulatory controlling method, the rate of
molar feed flow is escalating by 10 per cent of its first value. Therefore, the regulatory
controller’s performance at various set points is illustrated in Figure 4. The figure shows how
novel stable state purity escalates when the temperature is decreased. The temperature
necessary to get 99 per cent molars distillate purity is 342.442K [22].
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FIGURE 4 DISTILLATE PURITY VERSUS TIME, WHEN FEED FLOW RATE IS INCREASED BY 10% OF ITS INITIAL VALUE FOR
DIFFER TEMPERATURE SET POINTS [22]
Moreover, targeting over 99 per cent molar distillate purity, the controller is assessed for the
servo-controlling plan. The controller’s performance is illustrated in figure 2.6. Additionally,
it demonstrates the fact that object purity could be attained when the point is set at 342.03 K
.
FIGURE 5 VARIATION OF PURITY AND TEMPERATURE WHEN REQUIRED DISTILLATE PURITY IS 99% [22]
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3.5.2 COMPOSITION CONTROLLER
Methyl Cyclo Hexane (MCH) Composition controller refers to a relative integral controller
that can yield result purity at the preferred level through manipulation of the reboilers heat
input. We can analyze the degrees of freedom to establish how many and which control
parameters it is possible to control and manipulated. Then we can be desired how we can
control the two parameters: the top of the column composition and the pressure.
FIGURE 6 DISTILLATION COLUMN WITH COMPOSITION CONTROLLER [19]
After the tuning procedure, the controller undergoes testing to determine regulatory and servo
issues. The tuned controller parameters are could be obtained from the rate of molar feed
flow from 1.0 kmol/sec. Within the servo control scheme, the controller undergoes testing for
the targeted significance of 99 per cent distillate purity, as shown in Figure 7 below.
FIGURE 7 VARIATION OF THE PURITY OF THE DISTILLATE FOR THE COMPOSITION CONTROLLER WHEN 99 MOLAR PERCENT
OF PURITY IN THE DISTILLATE IS SET [19]
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After conducting servo tests for the controller, the controller undergoes testing for regulatory
control plan by altering the flow rate of the feed by 10 per cent from the initial value
(1kmol/sec).
Therefore, the controller performance is illustrated in Figure 8 that demonstrates that
following 6.65 hours the augmented load has been detached, as well as the controller attained
a novel value of steady state value (0.95 distillate mole fractions)
FIGURE 8 DISTILLATE PURITY FOR THE COMPOSITION CONTROLLER WHEN THE RATE OF MOLAR FEED FLOW IS RAISED BY
10% OF ITS FIRST VALUE [19]
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4. Software Overview
The project concerns co-simulation of the controller designed for distillation column through
the use of Aspen Plus, Aspen Dynamics, in addition to MATLAB Simulink because the
diverse software packages utilized has got various capacities along with functions in the
project. This part of the project will describe the software, which has been utilized to
undertake the project.
4.1 Aspen Plus
Aspen Plus is a software that contains a large-scale of database characteristics together with
inbuilt models for multifaceted unit operations that include distillation columns. The software
is utilized for the stable simulation where it is utilized to identify the primary conditions for
the dynamics simulation, ascertain material along with energy equilibrium, as well as
conceptual design. This is performed before operating the dynamic procedure, where the
stable model is transferred to Aspen Dynamics from Aspen Plus to permit the procedure to
flow into the dynamic process. In the instance, this is vital to make sure that the Aspen Plus
obtains the accurate, steady state value before going on with the next steps. Therefore, this
implies that provided the value is incorrect, the outcome of the procedure will be wrong,
where it will impact the entire procedure. Regrettably, Aspen Plus may only carry out steady
state simulation, in which it needs other software versions to run the Dynamic simulation [5].
The difference between DSTWU, Distl, and RadFrac are described in detail below [6].
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DSTWU The distillation column kind DSTWU is tailored for single feed procedure. Besides, the
manner where DSTWU runs through eliminating the lowest number of phases of the
distillation column along with the lowest value of reflux ratio of the process. This will be
followed by the computation of the needed reflux ratio founded on the user unit. Similarly,
Aspen Plus has the role of estimating the finest phase’s site, the condenser along with the
reboiler.
Following that, it would compute the mandatory reflux ratio founded on user input; this
means that the outcomes may be observed when Aspen Plus has performed the calculation
[6].
Distl The distillation column type Distil is tailored for a single feed process where it functions by
utilizing Edmister approach. Distil would compute the product composition of the procedure.
Therefore, the users should input the number of column designs [6].
RadFrac RadFrac is a kind of distillation colum that has been tailored for multiple feed process, where
the columns are more rigorous as compared to Distil and DSTWU columns. Thus, the
compressed design on the RADFRAC column in Aspen Plus permits the modeling of
condensers along with reboilers to be considered in the column’s qualifications. This
eradicates the requirement for the pumps, reflux reservoirs, heat streams, in addition to heat
exchangers [6].
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5 Process modelling and simulation in Aspen Plus
The Aspen Plus is a common simulation software that is used in the sector and appropriate
for the project [5]. Aspen Plus has an advantage due to its capability to resolve problems that
entail multiple computations, in which several equations employed, are very complex. In
many cases, it is complex and nearly impractical to resolve these problems manually because
of human errors along with time constraints. Aspen Plus is regularly utilized in industrial oil
production, refining, as well as environmental studies.
Aspen Plus may forecast the behaviour of a procedure from “engineering associations like
mass plus energy equilibriums, phase in addition to chemical equilibrium and reaction
kinetics [8]. Having feasible operation and reliable framework, it permits procedure along
with control engineers to simulate the procedure such as the actual plant.
Every process has its process system, in which Aspen Plus is utilized for the process model.
Three steps are available to get the process model, which include flowsheet that specifies the
chemical composition, as well as operating conditions. The third step, Aspen Plus functions
in line with all designs plus simulations entailed in various procedures of the project. Finally,
Aspen Plus helps to forecast the behavior and computes the findings of the system.
Additionally, it predicts the conduct and calculates the outcome of the system. Aspen Plus
will list the results for every one of the streams and the unit when the figuring is finished [9].
1. Flowsheet
The process flowsheet model procedure will mirror the whole system where it illustrates the
inlet streams that enter the unit operation (column, reboiler, along with heat exchanger) in
addition to outlet streams that come from the unit operation. Consequently, all bay streams
plus exit streams may be recognized [9].
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2. Chemical Components
In Aspen Plus, the chemical constituents must be described before shifting to the subsequent
phase. Every chemical component that is utilized in the process should be described in detail
to enhance the simulation process [9].
3. Operating Conditions
Usually, all the working units contain specific working circumstances like temperature, as well as
pressure. This is ascertained based on the working environment of the entire procedure [9].
5.1.1 Component Selection Based on the case study, the emphasis is on the component selection for Aspen Plus with the
reason to recognize and comprehend the method for the constituent choice that has been
undertaken through deliberation of an example in Aspen dynamics.
Once the structure (Figure 9) has been completed, the chemical components and its
properties of the process should be selected. Before defining the chemical elements, there is
the need to set the physical properties, define the parameters of the equipment, as well as
define properties of the stream.
FIGURE 9 BASIC FLOWSHEET OF THE PROCESS
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Toluene, Phenol, and Methyl Cyclo Hexane (MCH) are the three main components utilized in
the thesis as revealed in Finger 10 below.
These components should be described in Aspen Plus to permit the components utilized
Aspen Plus has a widespread databank of components utilized, which comprises their
physical features. Consequently, it can detect the materials utilized along with filling the
requisite space routinely. The comprehensive data regarding feed stream plus product stream
is clearly shown in Table 9: Feed stream in Table 8: product stream correspondingly, in
Appendix A – Results.
FIGURE 10 COMPONENT SELECTION OF THE PROCESS
From the example in Aspen Plus, The process input streams should be specified where there
are two input streams: the feed stream PHENOL along with MCH-TOL. Thus, the flow rate,
structure, temperature, in addition to the pressure of this stream should be detailed.
Additionally, there is the need to permit for some pressure decline in the control valve
located on the feed stream. As a result, there is the need to assume that there should be a 5
psia pressure plunge. Consequently, the composition of MethylCycloHexane (MCH),
Toluene utilized a temperature of 220 ℉ plus the pressure of 20 psia while the Phenol utilized
a temperature 220 ℉ and pressured 20psia as illustrated in Table 1 below.
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TABLE 1 PHENOL & MCH-TOL STREAMS SPECIFICATION
Component Temperature [F] Pressure [psia] TOLUENE 220 20 PHENOL 220 20 Methyle Cyclo Hexane (MCH) 220 20
Molar flow rates along with the components is dis distillation calculation play a leading role.
Hence, the feed composition might be entered based on mole or mass portions, or it may be
based on the molar or mass flow rates in the process, where, Mole-Flow is selected as the
composition.
5.1.2 Selection of Distillation Column The selection of the column was undertaken using Aspen Plus. Nonetheless, the selection of
the column was researched to comprehend the rationale of choice and for the goal of learning.
From the numerous alternatives of the distillation column that include DSTWU, Distl, as well
as RadFrac. These alternatives have diverse purposes plus abilities. To simulate the
distillation columns having reboiler and reflux, RADFRAC columns were applied in the
process. Furthermore, as it specified in Aspen Plus RADFRAC is more accurate as compared
to DSTWU columns plus appropriate for extractive distillation column for permitting
manifold product with feed streams.
Furthermore, the compressed model on the RADFRAC column in Aspen Plus permitted the
modelling of condensers along with reboilers to be considered in the distillation column’s
requirements. The primary is the configuration where the number of full phases, kind of
condenser, reboiler, the arithmetical convergence technique, as well as two other variables
are detailed. There is the need to consider the following:
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1. Number of stages: the meticulous manner to choose the number of phases is to
undertake an economic optimization, which is to find the maximum and
minimum points of the process. Also, the procedure would pick 22 stages.
The Aspen Plus utilizes the tray numbering principle of describing the reflux
drum as phase 1. The peak tray is phase 2 and other on the base of the
distillation column. Thus, the bottom of the column in the procedure is phase
22. Consequently, it implies that the column contains 20 trays.
2. Condenser: employ the drop- down to pick the Total. However, if the resultant
distillate is eliminated in the form of gas or vapour, then Partial- Vapour must
be preferred.
3. Reboiler: from the Aspen Plus, both the kettle plus thermosiphon reboiler are
fractional reboiler, therefore, selecting any is not an issue in this case. Plus,
kettle works best out of the two, because of the increasing rate and varying
process.
4. Convergence: The Standard technique functions effectively in hydrocarbon
set-up.
5. Operating specifications: There exist three degrees of freedom (𝑁𝑁𝑑𝑑𝑑𝑑𝑑𝑑) in the
distillation column once the feed, pressure, quantity of trays, also, to feed tray
sites have predetermined. As shown in Equation 2 below the degree of
freedom (𝑁𝑁𝑑𝑑𝑑𝑑𝑑𝑑) is the subtraction of the independent variants (𝑁𝑁𝑣𝑣𝑣𝑣𝑣𝑣) from the
number of equation (𝑁𝑁𝑒𝑒𝑒𝑒). It is important to find the relationship between the
number of working parameters (𝑁𝑁𝑤𝑤𝑑𝑑𝑣𝑣𝑤𝑤) and degree of freedom (𝑁𝑁𝑑𝑑𝑑𝑑𝑑𝑑). Which
will lead to three outcomes 𝑁𝑁𝑑𝑑𝑑𝑑𝑑𝑑- N_work ≥0, may have set of solutions and
25 | P a g e
are the infinite number of solutions if it's equal to zero or more than zero
respectively and if its less than zero there want to be any solutions. Many
optional means to select the two degrees of freedom are accessible as
illustrated in Figure 9. In the simulation phase, the normal approach entails
fixing the distillation flow-rate, as well as the reflux ratio. For the Distillation
rate 200 lbmol/hr, since we recognize the Mole-Flow rate of MCH in the feed.
Now, let's choose the Reflux ratio. When all the needed input data has been
attained, red dot on DIST will turn blue.
EQUATION 2 DEGREES OF FREEDOM
𝑁𝑁𝑑𝑑𝑑𝑑𝑑𝑑 = 𝑁𝑁𝑣𝑣𝑎𝑎𝑣𝑣 − 𝑁𝑁𝑒𝑒𝑒𝑒
FIGURE 11 DISTILLATION COLUMN CONFIGURATION
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Second, on stage 7 for Phenol and stage 14 for MCH-TOL stream, there is the need to set
three stages. Finally, there is the need to stipulate the pressure needed in the reflux along with
the pressure decline all through each of the tray in the distillation column. The reflux drum
pressure is set at 16 psia while the column pressure declines at 4.2 psia. Once that tab is
completed. All the tabs in the distillation column been specified without any errors.
5.1.3 Valves and Pumps In this case, at the start, both pumps produce a pressure variation of 6 psia amid the discharge
and suction pump. Hence, pump 11 with “Pressure Increase” button chosen along with the 6
psia pump head entered P12, which is treated similarly.
Also, the pressure at the exit of the feed valve (V1) should correspond to the feed tray
pressure (stage 7), while the exit pressure of the valve (V2) should correspond to the pressure
that exit on feed tray (stage 14). In this case, 14.2 psia was selected for both valve 1 and 2.
Moreover, the “Flash options” should be transformed to “|Liquid-Only” that has nothing
about the steady-state simulation; however, it is vital for the dynamic simulations during the
process. When the simulation works, and from the result sheet, the fall in the pressure of the
two stages is known. Ones we simulated the stable state we have ascertained the outlet
pressure of the valve 1 and 2 positions being 17.2 psia & 18.6 psia of V1 and V2
respectively, as shown in Appendix A Table 11.
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5.1.4 Steady State Simulation Thus, the steady state distillation column simulation was undertaken, as well as the summary
of the findings represented in Table 3 beneath. The complete findings may be visible in
Figure 31 in Appendix A -Results section.
There exist four streams as illustrated in Figure 9, where streams 1 and 2 are the feed inlets
to column, D is the liquid distillation that leaves the reflux drum. On the other hand, stream B
is the liquid bottoms that leave the bottom of the column.
TABLE 2 STEADY STATE RESULT
Units BOTTOMS Distillation MCH-TOL PHENOL Sub stream: MIXED Phase Liquid Liquid Liquid Liquid Component Mole Flow PHENOL Ibmol/hr 1197.0 2.9566 0 1200 MCH Ibmol/hr 3.44262 196.557 200 0 TOLUENE Ibmol/hr 199.514 0.486027 200 0 Component Mole Fraction PHENOL 0.855031 0.014783 0 1 MCH 0.002459 0.982787 0.5 0 TOLUENE 0.14251 0.002430 0.5 0 Mole Flow Ibmol/hr 1400 200 400 1200 Mass Flow kg/sec 16.5534 2.47241 4.7962 14.2297 Volume Flow cum/sec 0.0179509 0.00354 0.00650 0.01396
5.1.5 Sensitivity Analysis Sensitivity analysis has been used to learn the unreliability of the output of the system can be
allocated to various sources of unreliability in its inputs. In the case of Aspen Plus, the system
to the present process may be fashioned to determine if there is any form of uncertainty.
Sensitivity analysis may be undertaken when it has been determined that there are no errors
in the process. Definition of the variables follows after the creation of new ID. In the figure
below, X, QC, and Q variables are defined. It is clear in the figure below that MCH stream
been defined by the X variable. Ones the stream has been defined we have to specify the
characteristics of the blocks which related with the stream.
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Block QC has been described and Reference Variable been nominated as COND-DUTY and
the dissimilarity amid two blocks QC and Q is the Reference Variable, which is illustrated in
Figure 12, where Variable been transformed to REB-DUTY.
FIGURE 12 SPECIFICATION OF SENSITIVITY ANALYSIS
Furthermore, once the variables have been defined, there is the need to manipulate the
variable. The Phenol chosen for the case as the variable and its boundaries is set to 800
lbmol/hr and 2000 lbmol/hr as a Start point and Endpoint correspondently, which can be
different from process to process. To meet all the inputs, there is the need to allocate
“Number of Points” to run the simulation. Therefore, the number of points have been inputted
as 10. Once this has been met, then the selection of all variables follows and to meet the
sensitivity analysis of the specified process as illustrated in Table 12, in Appendix A-
Results.
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Figure 13 shows the results in Table 11, purity against phenol flowrate. Thus, it may be
observed from the figure below that rising Phenol flow-rate will increase the purity of the
distillate from the process. This implies that if it is essential to design another control loop to
manage the purity of the distillate through manipulating the flow-rate of Phenol may be
chosen as MV.
It can be seen from the figure that increasing phenol flowrate will increase the distillate
purity. Hence, this means that if it is essential to building up another control loop to control
the distillate purity by modifying the phenol flowrate can be selected as MV.
FIGURE 13 SENSITIVITY RESULT CURVE
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5.1.6 Design Specification
When sensitivity analysis has been performed, the design specification for the process is
developed. The target can be set for the process and determine if the process will satisfy the
distillation stream. Therefore, on the case of “Flow sheeting Options” X variable based on
specifications for the stream, MCH is created on the left side window as described for the
sensitivity analysis. The “Target” and “Tolerance” level for the variable is defined after the
specification of the X (stream MCH) variable as illustrated in Table 3 below.
TABLE 3 DESIGN SPECIFICATION
Design specification expressions Specification X Target 0.98 Tolerance 0.0001
After we define the specification of the entire process, it is essential to define the variable
stream for the process under study. PHENOL as the stream is nominated for this instance,
where the lower and upper boundaries will be 3,000 and 2,000,000 correspondingly.
5.1.7 Column Sizing
Length
Computing the height of the distillation column is relatively simple because we recognize the
number of trays present in the column. The archetypal length between trays, that is, the
spacing of the tray is 0.61 meters. If there exists NT stages, then the quantity of trays is NT-2
(one for reflux drum along with reboiler). Hence, a design heuristic is designed to offer an
extra 20 per cent of the height than the needed for just the trays [21]. Accordingly, the length
of the vessel may be approximated from the equation below:
𝐿𝐿 = 1.2(0.61)(𝑁𝑁𝑇𝑇 − 2)
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Diameter
The diameter of the distillation column used in the process is ascertained by the optimum
vapor volume. Thus, if the velocity is surpassed, the column liquid, as well as the vapor
hydraulics would fall short besides the distillation column would flood. Consistent
relationships are obtainable to determine this optimum vapor speed [21].
When the flow of vapor changes from one tray to another in a non-equimolal overflow
process, the tray with heights vapor speed would set the optimum column thickness.
Recognizing the mass flow of the vapor along with its density, the volumetric rate of flow of
steam may be easily computed. Subsequently, recognizing the optimum permissible speed,
the cross-sectional can be calculated more easily [21].
Aspen Plus contain friendly tray sizing capacity for the user. There is the need to divide the
distillation column into three sections while sizing the column. There will be three stages:
stage CS-1that run from tray 2 to 7; stage CS-2 that run from tray 8 to14; and CS-3 that run
from tray 15 to 21 as illustrated in Figure 14 below
FIGURE 14 COLUMN SIZING SPECIFICATION
From the hydraulic plot Figure 34 (Appendix A- Results), which display all the operating
points of the column that some of the stages are accomplished having an error that has been
marked using yellow colour and those in blue is completed with no errors.
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The CS-2 and CS-3 stages are the ones that are completed with errors due to the pressure
level, which is greater than what it may support to be at stage CS-1 that is between 7 and 14.
To eliminate these errors, there is the need to modify the pressure level of the stage. The only
problem here comes when Aspen dynamics is affected where the pressure decline to meet the
specification of the system.
5.1.8 Optimization Optimization issue of equivalent significance is the "rating problem" that is, finding the ideal
operating conditions for a given column with an exact number of stages. Optimization is used
to optimize or reduce a user-specified goal work through modifying the decision variables
that include block input, feed stream or other contribution variables. Accordingly, the goal
function may be any valid FORTRAN expression that entails one or more flowsheet
magnitudes [21]. Therefore, the tolerance of the objective function involves the tolerance of
the convergence block linked to the optimization issue.
One has the alternative of commanding either parity or disparity constraints on the process of
optimization. Parity constraints in each optimization are the same as design provisions in the
system. These constraints may be of any purpose of flowsheet variables calculated utilizing
FORTRAN expression or in-line FORTRAN statements [21]. Besides, we must state the
tolerance of the constraint.
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Additionally, the tear streams along with the optimization problem may be converged
concurrently or disjointedly, in which the tear stream is considered as an extra constraint.
Aspen Plus resolves optimization issues iteratively. From the default, Aspen Plus produces,
as well as sequences a union convergence for the optimization dilemma. In this instance, one
may supersede the convergence default, through entering convergence specifications on the
convergence forms provided.
The value of the manipulated variable, which is offered in the Stream or Block input is
utilized as the primary approximate. Therefore, supplying the finest approximate for the
modified variable aids the optimization converges in less iteration. This is particularly vital
for optimization problems that have huge numbers of diverse variables along with the
constraints.
It has been established that there exist no outcomes linked unswervingly to an optimization
dilemma, except for the objective function. One may perceive the ultimate value of the
modified and sampled variables straight, on the suitable stream or block result sheets. To
explore the summary, as well as iteration history [21].
Thus, issues of the optimization may be complex to create plus converge. It is imperative to
have a high-quality comprehension of the simulation dilemma before adding the difficulty of
optimization. During this process, two input and output streams are obtained that is defined as
FW1, FW2 and DW, BW correspondingly and finally the reboiler been described as QR.
These the variables that have been defined are illustrated in Table 4 below under column
Block/Stream.
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TABLE 4 OPTIMIZATION VARIABLE DEFINITION
Variable Category Type Block/Stream Sub-stream Variable Sentence Units FW1 Streams Stream-Var PHENOL MIXED MASS-FLOW - kg/sec FW2 Stream Stream-Var MCH-TOL MIXED MASS-FLOW - kg/sec DW Streams Stream-Var D MIXED MASS-FLOW - kg/sec BW Stream Stream-Var B MIXED MASS-FLOW - kg/sec QR Blocks Block-Var DIST - REB-DUTY RESULTS Watt
When these variables have been defined, as shown above, the “PROFIT” is set to be
optimized based on the constraints. Subsequently, it will be essential to create an equation for
the profit as shown below, which been taken from the Aspen Plus example itself.
PROFIT = DW*0.528 + BW*0.264 – FW1*0.264 – FW2*0.264 – QR*4.7e-9
Also, the last stage of the optimization process entails the specification of the modified
variable. The final stage of the optimization is specifying the manipulated variable, where
reflux ratio is selected for this case to optimize profit. Consequently, the manipulated variable
is illustrated in Table 5 below.
TABLE 5 OPTIMIZATION VARIABLE SPECIFICATION
Manipulated variable Variable limits Variable 1 Lower 2 Type Block-Var Upper 8 Block DIST Variable MOLE-RR
After optimization of the variable, simulation is run and that demonstrate the optimizing
algorithm is chronological quadratic programming (CQP), which takes four iterations to
discover the optimum profit. The resultant values of the variable are shown in Table14 show
in Appendix A-Results.
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5.1.9 Extractive Distillation
When the phase equilibrium is not near the ideal system, there is the need to use an
alternative convergence technique in the columns. In the distillation column, it is clear that
the “standard” convergence technique may be modified to “Azeotropic” or “Strongly non-
ideal liquid” that will advance convergence. The other change that is often essential is to alter
the optimum number of iteration, in which the default number is 25. This will be possible by
clicking the “Convergence” item located in the column block, as well as entering a bigger
number.
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6. Dynamic and process control in Aspen Dynamics
Aspen Dynamics entails a dynamic process simulator that is utilized to comprehend the
dynamic conduct of the entire procedure. Aspen Dynamics is strongly incorporated with
Aspen Plus, in which it is a simulator for a stable state. Thus, this permits the present stable
condition from Aspen Plus simulation to fashion a dynamic simulation [7].
The process dynamics implies the circumstance is shifting. This means that the procedure
changes in a given time. Particularly, this means that when the input of the procedure is
shifting, the way the output variable could retort over time. In many cases, process dynamics
handles a methodical categorization of the time reaction of the impacted variable to a
transform in the causal variable. Also, the affected variable is also called the output variable
while the causal variable is also called the input variable [7].
Aspen Dynamics permits the users to attain a detailed comprehension of the dynamics of the
processes. Thus, this understanding can be used by users to create and operate with optimal
security attaining stable product excellence along with the operability of the procedure. This
implies that a linear state space framework may be obtained from Aspen Dynamics utilizing
the control plan boundary in Aspen [10].
Before commencing the simulation process, the dynamic procedure in the Aspen Dynamics
software, a stable state simulation should be performed earlier located in the Aspen Plus
software. After the completion of the steady-state simulation happening in the Aspen Plus, all
the essential outcomes may be extracted, and tabulated in the graphs, in which it will
demonstrate behavior that corresponds to specific inputs. The resultant data is then
transferred into Aspen Dynamics.
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The sizing of the equipment like column width, size of the vessels, the spacing of the tray,
trays active area, weir length in addition to height, reflux drum altitude and length, and
reboiler altitude and span is the information required for Aspen Dynamics. The instrument
referred to as tray sizing offered by the Aspen Dynamics may be utilized to compute the sizes
of the tray founded on the flow environment in the distillation column; however, the sizing of
the trays may too be undertaken in Aspen Plus [11].
The procedure would then be tested using the loop plus closed loop system. Therefore, this is
performed to investigate the variations and effects amid these two kinds of the organization in
the procedure. Besides, the PI controller is utilized to control all the variables in the entire
system and needs some data, which it may be fine-tuned appropriately plus the data is
collected via process detection. There will be the need to set point change in every controller
and disturbance change would be introduced to specific variables to investigate the effect of
modifications on the purity of the product. This will be followed by a novel composition
controller would be designed to control the rate of flow of Phenol since its impacts the purity
of the product.
6.1 Dynamic Simulation When it is understood that there are no impacts on the steady-state solution; consequently,
there is the need to return to dynamics of the DIST block and modify the weir height from
around 0.05 to 0.025 meters and the slump on the dynamic and change the altitude, as well as
the diameter to 10.16 and 5.08 meters correspondingly. Eventually, one should change the
reflux drum length and diameter to 8.16 and 4.08 meters correspondingly.
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Once all the input data will have been revised to the simulation and if there will be no
warring in the input data one can do a “Pressure Check” on the process, and one may see the
message below. Thus, one can effectively export Aspen Plus into Aspen Dynamics.
FIGURE 15 DYNAMIC PRESSURE CHECK
In dynamics, it is important to that the integrator is functioning properly. This can be attained
through modifying “Initialization” back to “Dynamic” and undertaking the simulation
process. The original flowsheet has some default controllers by now fitted. Hence, in this
single-column process, there exists one default controller, the pressure controllers. This is
configured to quantify condenser pressure and modified condenser heat elimination. Hence,
the activity of the controller, the variety of the pressure transmitter, the optimum heat
elimination rate, as well as the controller ensuring that the constants are set up some minimal
value. At a nominal value, five extra controllers should be introduced to attain effective
operation of the column, which includes:
1. Reflux drum level controller
2. Base level controller
3. Feed flow controller
4. Tray temperature controller (TC)
The are other five manipulated variables that are not explored (distillate flow rate, bottom
flow rate, reboiler duty, as well as two feed flow rats) to control the five controlled variables
[21].
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6.1.1 Basic Level Controlles
Following the success of the dynamic simulation, where the process has been effectively
transferred to Aspen Dynamics, the five fundamental controllers can be placed for the two
feeds levels, as well as the bottom product and distillate product along with one pressure
controller for the process. Hence, PI controllers introduced in the process is shown in the
table below. The process variable (PV) signal entails the variable that should be controlled.
The level controller is the final stage of the distillation column (stage 22). Second, there is the
need to link the control signal from the controller to valve 11 located in the bottom product.
Therefore, it is important to launch one controller during the time the process and observe if
there are any possible errors in the process.
TABLE 6 CONTROLLER PLACEMENT
PV OP Controller 1 (LC11) Stage 22 V11 Controller 2 (LC12) Stage 1 V12 Controller 3 (FC1) Molar flow rate V1 Controller 4 Molar flow rate V2
Designing the controller, as illustrated in the figure above, in which we design the controller
parameters, in which switch from the switch manual to automatic control, in which there is
the need to change the SPs. From its design, it offers the steady-state values of the base level,
as well as the control valve opening.
The activity of the controller must be “Direct” since if the level rises, the signal to the valve
must augment too to eliminate additional bottoms. Accordingly, in some distillation columns,
base level is managed by modifying a valve in the feed to the distillation column, where the
base level controller activity must be “Reverse”.
40 | P a g e
FIGURE 16 LC11 FACEPLATE
Because there is the need for proportional-only control, the controller gain is set equal to 1.5
while the integral time is set a 9999 min.
The pressure controller that had automatically designed when we began the Aspen Dynamics.
As shown in the faceplate of pressure controller Figure 17 below, the default controller
tuning constants are gain of 20, as well as an integral time of 12 minutes. It is important to
understand that all the controller output is not a “percentage of scale” signal transmitted to
the valve, but it is a heat-removal velocity in the condenser. This is the case of the controller
that has been designed to “Reverse” action. Also, as the pressure increases the output signal
of controller declines, which causes more heat to be removed from the system. The purpose
of the activity becomes apparent when we examine at the output of the controller is from the
nominal -14128804.3 W to an utmost 0. This is designed to comply with the Aspen
convention where heat exclusion is negative.
The only possible modification that we can implement for the pressure controller is to
transform to a more expedient pressure transmitter variety, for instance, in the distillation
column, the operational 18 psia. The pressure transmitter can be modified range to 15-20 psia
from a very broad range utilized in the default design. Certainly, the gain must be
correspondingly lowered.
41 | P a g e
FIGURE 17 PRESSURE CONTROLLER FACEPLATE
While designing the controller two from the distillate stream, similar technique as in
controller one is used. The only change that will be made will be in the process variable (PV)
in stage 1, as well as the operational point as valve V12.
42 | P a g e
For the last two controllers, it is paramount to establish two-feed stream controllers. In both,
the stream process variable would be the molar flow rate of the distinct streams of Phenol and
MCH-TOL. The operational point will shift to V1 and V2 respectively, which is illustrated in
the flowsheet below. The controllers are designed to use a “Reverse” action, as well as the
conventional flow controller tuning is utilized (gain = 0.5 and integral time = 0.3 minutes).
FIGURE 18 FLOWSHEET OF THE BASIC CONTROLLER
43 | P a g e
6.1.2 Basic level Controller Tuning
In the earlier section, we have introduced four controllers to the process as showed in Figure
18 and its controller placements been given in Table 6. First, we must ensure that all the
controllers are tuned to make sure that the control parameters attain its finest value to
accomplish the best possible control response of the system. Hence, all PI controllers were
tuned separately utilizing the 'Tuning' instrument [24]. The 'Tuning' is a unique instrument
accessible in Aspen Dynamics to carry out auto-tuning. Thus, to utilize the 'Tuning', the “Test
Method” should be nominated to the “Closed Loop ATV” before the testing, which is
illustrated in Figure 19. Therefore, when the testing has begun for the controller, the PV in
addition to the manipulated variable response of MethylCycloHexane (MCH) purity was
recorded.
When 10 seconds elapses, the “Calculate” must be keyed into the “Tuning” begin computing.
The “Tuning” would provide the best possible parameters to the respond of the control.
Subsequently, the parameters which been obtained from the tuning been sent (Figure 20) to
the controller and the process been simulated again. Appendix A – Figure 37, LC11 chart
shows the controller has reached its desired set-point.
It is clear from Figure 20 that the “Tuning Rule” is an alternative that enables the users to
select the kind of tuning technique they want to use. Ziegler Nichols plus and Tyreus-Luyben
tunings remain the most accepted techniques which been used in Aspen Dynamics [21].
Ziegler Nichols tuning method is reasonably aggressive when contrasted to Tyreus-Luyben
tuning technique, which is relatively quite loose. Thus, Tyreus-Luyben is characteristically
utilized for distillation column to evade the massive, rapid increments or aggressive changes
in the procedure. For instance, the reboiler task would result in hydraulic challenges if the
combative changes take place on the reboiler. As a result, Tyreus-Luyben is a more
traditional tuning technique contrasted with Ziegler Nichols in this type of process [19].
44 | P a g e
Similar steps are used to tune all the controllers in the process. Table 7 illustrates the
parameter outcomes accrued from the tuning process. Appendix A – Figure 37, shows that
all the controllers are perfectly tuned, and the process reaches its desired setpoints.
FIGURE 19 MCH TEST TUNING CONTROLLER
FIGURE 20 TUNING CALCULATION
45 | P a g e
TABLE 7 CONTROLLED PARAMETERS
Proportional Gain,𝐾𝐾𝑐𝑐 Integral Time Constant,𝜏𝜏𝑖𝑖 Feed Tank Level (FC2) 3.125 22 Reboiler Level (LC11) 0.625 22 Reflux Drum Level (LC12) 94.32 3.96 Top Stream Pressure (PC1) 1.5625 22 MCH (Product) Purity (FC1) 358.537 160.2
6.1.3 Tray temperature control The purpose of the temperature controller is to maintain the temperature of the tray in the
distillation column. Several ways have been proposed when it comes to selecting the tray to
hold the constant temperature. The largest temperature change from tray to tray (“slop” of
the temperature profile), use of singular value decomposition, picking a tray where the
temperature wants to change when the composition changes when the producing the desired
distillate and bottoms purities. In this case study, we use the largest temperature change from
tray to the tray to determine which tray to be controlled. It is apparent from the Table 10 of
the Aspen Plus that the Stage 21 and 22 exhibits a comparatively largest slope. The
temperature of stage 21 and 22 is 405.089 K & 423.738 K correspondingly.
The controller is set up on the flowsheet in the usual manner. The PV is chosen to be the
temperature on stage 21, while the OP is chosen to be the reboiler heat “OrebR”. Figure 21
displays the controller faceplate of the temperature controller. Thus, the normal controller
output is 277777W. This means that the controller activity must be installed at “Reserves”
since if the temperature of the tray is increasing, the reboiler heat input must be lowered. It is
recommendable to modify the variables of the temperature from the normal default of
273.1K-501.7 K to a more expedient and narrow range of 400K-450K.
46 | P a g e
FIGURE 21 TEMPERATURE CONTROLLER FACEPLATE
Following the verification to the fact that there are no lags or a dead time present in the loop,
and then it can be backed up and inserted a dead time element on the flowsheet between the
distillation column and the temperature controller. This is important because installing the
controller originally with no dead time element means avoiding initialization challenges.
This stage involves the installation of the dead time element. The control line from stage 21
temperature is chosen for this purpose. A new controller that is installed signal should be
inserted between the dead time and the controller. The dead time value is originally 0 minutes
as illustrated in the figure below. Thus, the input and output standards are designed as a
default number that moves away from the real 405.089K and a dead time value of 1 is keyed
in as its suggest in Aspen Plus [19].
FIGURE 22 DEADTIME TABLE
47 | P a g e
FIGURE 23 FLOWSHEET WITH THE TEMPERATURE CONTROLLER
6.1.4 Relay- Feedback Test When all the process has been run without errors, the relay-feedback test can be performed.
The test to generate sustained oscillation as an alternative to the standard continuous cycling
technique. It is an extremely successful way of deciding the ultimate gain and ultimate
frequency. This is done in the same manner it was in the composition controller where the
closed loop ATV (auto-tune variation) is the test technique specified. In this case, the default
rate of the relay output amplitude is 5 per cent that is reasonably a good value. There is the
need to lower the amplitude for every nonlinear column.
48 | P a g e
FIGURE 24 RELAY-FEEDBACK TEST RESULTS
49 ASPEN PLUS & ASPEN DYNAMICS
FIGURE 25 CALCULATED TEST RESULTS
Once the start test has begun, let it run many cycles as shown in Figure 24. At this point, the
predicted ultimate gain of 639.24 is seen while the ultimate period will be 7.2 minutes. Lastly,
the tuning parameters, the Tyreus-Luyben technique been utilized receives the computed
resultant controller settings being 𝐾𝐾𝐶𝐶 =199.7639 while the integral time 𝜏𝜏𝑖𝑖=15.84 minutes as
illustrated in Figure 25. The Tyreus-Luyben tuning formula is shown below [19]:
𝐾𝐾𝐶𝐶 = 𝐾𝐾𝑈𝑈3.2
𝜏𝜏𝑖𝑖 = 2.2𝑃𝑃𝑈𝑈
When the values computed have been sent to the controller, the controller can be seen to have hit
its steady-state point as illustrated in Figure 26 below. To authenticate the controller functions to
its perspective, one may initiate a set change plus see if the PV reaches its preferred point.
50 ASPEN PLUS & ASPEN DYNAMICS
As a result, the temperature will be modified from 392 K to 400K, and it may see from Figure
26 below, which it has reached it SP that verifies that the tuning of the temperature control in
stage 21 is operates to its perspective.
FIGURE 26 TUNED TEMPERATURE CONTROLLER
6.1.5 Composition Controller At this point, a comparison of tray temperature with two other kinds of composition control is
undertaken. This is where the composition distillate MCH product is quantified directly and at
the same time controlled at 1200 Ibmol/hr Phenol impurity.
The original type of composition control is direct composition control where the single PI
controller is utilized with the reboiler heat input modified. The other kind of composition control
utilizes a case composition-to-temperature control model. The composition dimension classically
has bigger dead time and lags as compared to temperature control structure. Hence, it may be
assumed that it has a 3 minutes dead time in the composition movement.
390
392
394
396
398
400
402
404
406
0 50 100 150 200 250
Tem
pera
ture
K
Time (Hours)
Temperature controller
PV K SP K
51 ASPEN PLUS & ASPEN DYNAMICS
First, like other controllers that have been added to the process, the PI controller may be added
without the dead time in the beginning.
The controller must be “Reverse” since the PV is a mole fraction of Phenol impurity present in
the distillate stream while the OP is reboilering heat input. When there is excess phenol going in
to the top of the distillate, then the reboiler heat input must be lowered. A composition spreader
range of 0-0.05 mole fraction Phenol is employed as illustrated in Figure 27 below.
FIGURE 27 COMPOSITION CONTROLLER FACEPLATE
52 ASPEN PLUS & ASPEN DYNAMICS
Following the simulation process, it will be determined that if there are no errors in the entire
process, then 3 minutes dead time is added to the system. Consequently, a relay-feedback test is
performed on the system. The results of the controller are demonstrated in Figure 28 below.
Thus, the ultimate gain was 1.5625 while the integral time was 44 minutes. The Tyreus-Luyben
setting is computed, and the valves will be updated in the controller. From the Figure 30 below,
it can be ascertained that the composition controller has reached its SP, but with the given set
point change the controller was unable to reach its desired set point. Composition controller
tuning wasn’t easy to control because of the open loop time constant changes whenever we run
the simulation.
FIGURE 28 COMPOSITION TUNED CONTROLLER
53 ASPEN PLUS & ASPEN DYNAMICS
FIGURE 29 FLOWSHEET OF THE COMPOSITION CONTROLLER WITH THE DEAD TIME [∆T]
FIGURE 30 TUNED COMPOSITION CONTROLLER
0.40.45
0.50.55
0.60.65
0.70.75
0.80.85
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200
Com
post
ion
kmol
/km
o;
Time (Hours)
Composition controller
PV kmol/kmol SP kmol/kmol
54 ASPEN PLUS & ASPEN DYNAMICS
6.1.6 Set Point Change The setpoint change is utilized in a situation when the set point is anticipated to alter more often,
and PV is to amplify or decline based on the controller. By setpoint tracking, the performance of
the controller being utilized may be tested, whether it is too forceful or covering when a position
point change is created. Moreover, setpoint change is to analysis to impact its effect of set point
change to the purity of the process. Also, the testing of the process controllers was undertaken by
carrying out steps on the set points independently. The variations of ±5 %, ±10 % and ±25 % that
range from the steady state were employed as the step magnitudes. Thus, the outcomes of the set
point change proved in Figure 37 in Appendix A - Results. Nevertheless, not all outcomes of
the system have been provided; however, only the outcomes with observable change were
explained.
6.1.7 Disturbance Change Fundamentally, the impacts of disturbances on variables are studied through using a step change
to the disturbance at hand from steady state, as well as observing the manner variables response.
Nonetheless, the disturbance would only be initiated to single variables, in which it will have a
significant effect on other variables of the process. Consequently, the Phenol flow-rate along
with the coolant flow-rate to condenser has been chosen as the possible disturbance. These
would be managed in open loop, whereby the disturbance alteration would be initiated to the
modified variable with the step change of ±25 %. Accordingly, the feed level, pressure, reflux
intensity along with reboiler level would be managed through controller when the disturbance is
initiated in phenol flowrate, while feed level, reflux level, plus reboiler level would be checked
through the controller when the disturbance change is created in the coolant flow rate.
55 ASPEN PLUS & ASPEN DYNAMICS
7.0 Results and Discussion This part discusses some issues of the subjects that are connected to Aspen Plus in addition to
Aspen Dynamics.
7.1 Installing temperature and composition controller Additionally, installing temperature, as well as composition controllers is rather more engaging
compared to installing level in addition to flow controllers due to three matters.
1. It is essential to include further dynamic fundamentals in the loop during the process.
Temperature, as well as composition measurements, has substantial characteristic
dynamic lags along with dead times, which must be included in the control loop. This
will play a leading role since it entails the application of realistic controller tuning, in
which it does not predict the performance of dynamics, which is better as compared to the
performance attainable in actual plant installation in the whole process.
2. The modification of composition along with temperature composition is more involved
than basically utilizing heuristics like in the instance of flow, as well as level controllers.
Some efficient and expedient tunning process is needed. It has been established that the
excellent techniques are to operate a relay-feedback test to investigate the ultimate gain
along with ultimate rate. This kind of test is integrated into Aspen Dynamics and is
simple to operate. Therefore, understanding the ultimate gain time, some typical tuning
principles may be used in the process. The traditional Tyreus-Luyben tuning settings
function effectively in the case of distillation columns where aggressive reactions are
negative since they can lead to column flooding or dumping because of the hydraulic
flaws.
56 ASPEN PLUS & ASPEN DYNAMICS
3. The suitable site for the temperature or composition sensor cannot be clear in this case.
Some technique for undertaking a fine selection should be utilized. Hence, there are
numerous techniques to handle this challenge that comprise examining at the shape of the
temperature outline in the distillation column and computing steady-state gains.
7.2 DMC Design Parameter The Dynamic Matrix Control (DMC) is also a Model Predictive Control (MPC) method, which
was designed and coined by Shell Oil and is reflected to be the algorithm of the years during the
1980s [12]. The advancement of the model predictive control methods is established through
using the discrete-time convolution framework. Advanced control programs, such as this are
integrated into systems that show unusual dynamic behaviour.
MPC is a strategy in which a framework assists in the forecast of the prospect alterations in the
procedure by looking at the past control occurrences assisting in the optimization of the system
control signal [13]. Thus, the most widespread and comprehensive methods for MPC are those
that are founded on the objective function optimization that entails the error amid the set point
and forecasted outputs.
Furthermore, the dynamic matrix control has a different parameter, which may be modified to
attain the needed response. These parameters include sampling time, the model horizon, control
horizon, prediction horizon along with two weighing matrices designed for forecasting possible
errors, as well as the control moves correspondingly. The model horizon and sampling method
are originally defined because they are required to get step response data. The overall principle is
that the representation horizon multiplied through the sampling period must surpass the time
taken for the procedure to be 99% complete. Thus, the model horizon should be significant
enough so that it accommodates sufficient data on the dynamics of the scheme [14].
57 ASPEN PLUS & ASPEN DYNAMICS
8. Conclusion
Thus, we see that Aspen makes it easy to build and run the process simulation model by
providing with a comprehensive system of the online process modelling. Also, Process
simulation allows one to predict the behaviour of a process by using fundamental engineering
relationships, such as mass and energy balances, and phase and chemical equilibrium.
Sophisticated software’s like Aspen Plus and Aspen Dynamics studying them can be
complicated sometimes. But I would say that understanding and studying them for them was
done from the start of the project and been executed to the best outcome.
From the obtained results, we observe that with simulation; one can design a better process and
increase the profitability of the existing process, by implementing better controllers to process
and controlling its output. Process simulation is helpful throughout the entire life of a process,
from research and development through process design to production. Aspen Dynamics help in
getting information about the behaviour of installed control configuration. After tuning of the
controller, we checked the behaviour of controllers in regulatory as well as servo control
schemes. On observing the respective responses, we see that temperature control has a faster
response than the composition controller. Hence, we can conclude that Aspen Dynamics helps in
finding out the sensitivity of the controllers. In future; if it is properly interfaced with MATLAB
and DMC controllers, then it provides facility to control the more rigorous columns like solvent
recovery columns, which wasn’t properly executed in this study due to software compatibility
with Windows 10 and been left for the future students. If the link between the MATLAB and
Aspen Dynamics were built, it would have helped to explain the process bit better and
understand the controller behaviour for further improvements of the process. Moreover, DMC
58 ASPEN PLUS & ASPEN DYNAMICS
controllers been tried to replace the PI controllers, the implication of the advance controllers was
failed and need more analysis done on that to be implemented without any errors.
In this work, all the results are obtained from steady-state and dynamic simulations using
ASPEN PLUS and ASPEN DYNAMICS programs for MCH distillation column. Increased
phenol feed gives more purity of MCH. Although, without MATLAB and DMC controllers been
developed for this process, this project has given me great knowledge of the two software
packers. This two software’s are one of the common software’s in the industries which been used
for bulk chemicals, speciality chemicals and pharmaceutical industries.
59 ASPEN PLUS & ASPEN DYNAMICS
9. Future Work This part stresses on the prospective directions, which must be of the concerns by the next
students in this field working on this project.
9.1 Review the Composition Controller and the Dynamic Matrix Control (DMC) From the results obtained in Section The sensitivity analysis plus the impacts of Phenol flow-rate on purity demonstrate that they
have diverse flow-rate to attain the needed MCH purity. Contrarily, flow-rate from both parts
must be similar to attain the essential MCH purity. Thus, this challenge is presumed to be due to
the controller code configuration. Also, dead time can be introduced into a composition
controller because it aids to evade response limitation in the system during the process.
The dynamic matrix control did not develop in the case study because of the system displaying a
“linearization” error. Thus, this concern should be resolved to make sure that the process should
be managed by dynamic matrix control. This kind of error was possibly due to the issue of
setting, as well as configuring the diverse variables in MATLAB Simulink. Consequently, if this
challenge can be resolved, comparison control approaches in Aspen Dynamics along with the co-
simulation may be performed via performance criteria technique.
As a result, it is recommended that prospective student to attempt to resolve this primary issue
earlier than moving forward on any other suggestions.
9.2 Implementing the Solvent Recovery Column As discussed before, this ICE project comprises two main parts as entire; the extractive column
covers part one, and solvent recovery column covers part two of the project. Nonetheless, the
project majorly concentrates on part one of the project that has been tested with the steady state,
as well as the dynamic process. Thus, the second part entails the separation of product that was
obtained from part one, which includes Toluene and Phenol (extractant). Hence, Phenol would
60 ASPEN PLUS & ASPEN DYNAMICS
be recycled to the extractive distillation column for reuse, while Toluene could be the final
product. The steps that are undertaken during the second section of the project is similar as it was
in the first section in which it requires to be tested using open-loop and closed-loop systems, a
step change, as well as performance criteria.
9.3 Relative Gain Analysis (RGA) In practice, it must be acknowledged that the control loop combinations during the project were
selected from a rational premise other than embracing the relative gain analysis (RGA) strategy.
This was designed to make sure that time was expended on design, execution, as well as
investigation of the predictable controller is attained. Consequently, RGA is employed to
describe the finest input-output variable combinations. Although the utilization of a gain matrix,
it is feasible to derive the application of the pumps along with the valves for this project
regarding the effect every pump along with the valve has on one another.
For this part, the RGA would be founded on the distillation column, reboiler, as well as the
condenser. Before moving into the matrix computations, the gains of the pump along with the
valves should be considered. This should be undertaken by designing every pump along with the
valve separately. Hence, stepping the valves up independently permits the gains to be computed
at the temperatures, levels, which interrelate with it.
To compute the gains of every response, there is the need to utilize regression analysis (RA).
This analysis has been applied earlier in ascertaining the numerical frameworks for the system. It
has been found that when the gain value is accrued, they would be put in a gain matrix. This gain
matrix is then inversed, as well as transposed founded on RGA equation,𝑅𝑅 = [(𝐺𝐺)−1]𝑇𝑇, in which
G is the gain matrix along with T represents the transpose of the inverse matrix.
61 ASPEN PLUS & ASPEN DYNAMICS
Once the matrix has been inversed, as well as transposed, it would then be multiplied by the gain
matrix. Nevertheless, this is not a standard matrix computation because its element number
would multiply each element. An instance is founded on the matrix beneath, gain matrix. When
carrying out the multiplication, 𝑔𝑔11, would be multiplied by the equivalent value in the
transposed matrix, i.e. 𝑇𝑇11. Thus, this would happen for every element in the matrix [15].
EQUATION 3 GAIN MATRIX (G)
9.4 Aspen Custom Modeler (ACM) Aspen has developed various kinds of software like Aspen Plus, Aspen Dynamics along with
Aspen Custom Modeler (ACM). Regarding future works, there is the need to encourage future
learners may implement and execute this project in ACM since students may identify the
software and make a comparison with other kinds of software available. ACM is unique from the
software utilized in this ICE project; in which it does not need other software to perform steady
state process, dynamic procedure, as well as an optimization since it can perform it by itself. For
that reason, ACM software is very helpful to learners since it does not need diverse kinds of
software to undertake in addition to test a process. Different from this, learners will have the
capacity to analyses, as well as compare controller in ACM with software utilized in this project,
whether it was needlessly forceful or too lagging.
62 ASPEN PLUS & ASPEN DYNAMICS
10. Bibliography Luyben, W.L (2006). Distillation Design and Control Using Aspen Simulation, John Wiley & Sons, New York.
This book been used as the back born of this thesis. Understanding distillation column, implementation, basic concepts of the ASPEN PLUS and ASPEN DYANAMICS, individual columns etc. chapters 1 to 13 been used to develop the process.
B. A. Ogunnaike and W. H. Ray, Process Dynamics, Modeling, And Control, Oxford, New York: Oxford
University Press, 1994
The book been very useful and been used as reference study at Murdoch university and also in the thesis as well. The chapter 14 (feedback control system) and chapter 15 (conventional feedback control design) are the chapters which been referend on this book. Chapter 14, been helpful to understand the feedback controller and the chapter 15 for analytical methods.
aspentech, "Jump Start: Getting Started with Aspen Plus V8," AspenTech, Bedford, 2015
Aspen Plus is a very complicated software. And the to understand concepts of the software want easy with V10 version. This document helped to great extend to understand the software.
63 ASPEN PLUS & ASPEN DYNAMICS
11. Work Cited [1] I. D. Gil, D. C. Botı´a, P. Ortiz and O. F. Sa´nchez, “Extractive Distillation of Acetone/Methanol Mixture Using Water as Entrainer,” Ind. Eng. Chem. Res., vol. Vol. 48, no. 10, pp. 4858-4865, 2009
[2] 1rv07ch.files.wordpress, “Extractive Distillation,” 28 December 2015. [Online]. Available: https://1rv07ch.files.wordpress.com/2010/05/extractive-distillation.pdf. [Accessed 30 December 2017].
[3] HowStuffWorks.com, “Distillation,” 1 July 2009. [Online]. Available: http://www.howstuffworks.com/distillation-info.htm. [Accessed 28 December 2017].
[4] M. T. Tham, “Distillation: An Introduction,” October 1997. [Online]. Available: http://aussiedistiller.com.au/books/Chocaholic/Introduction%20to%20Distillation.pdf. [Accessed 28 December 2017].
[5] aspentech, “Jump Start: Getting Started with Aspen Plus V8,” AspenTech, Bedford, 2015.
[6] courses.washington.edu, “Aspen Tutorial #6: Aspen Distillation,” 2015. [Online]. Available: http://courses.washington.edu/overney/Aspen/Aspen_Tutorial_Unit_6.pdf. [Accessed 28 December 2017].
[7] aspentech, “Aspen Dynamics,” 15 February 2004. [Online]. Available: www.aspentech.com. [Accessed 29 December 2017].
[8] Aspen Plus, “Process modeling environment for conceptual design, optimization, and performance,” 2015. [Online]. Available: https://www.google.com.au/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0ahUKEwiflIDgl MLKAhVEJpQKHepLCL8QFggcMAA&url=https%3A%2F%2Fwww.aspentech.com%2FBrochure%2FAs penPlus.pdf&usg=AFQjCNE2moLFL44Pp81FJaU9PD3k_qVStA&sig2=LwlIdlHW_i2ZRprCjaP16Q&cad=rja. [Accessed 27 December 2017].
[9] umich.edu, “Aspen Plus Information,” 2007. [Online]. Available: http://www.umich.edu/~elements/fogler&gurmen/html/help/software/html/aspen/index.htm. [Accessed 28 December 2017].
[10] M. Khodadoost and J. Sadeghi, “Dynamic Simulation of Distillation Sequences in Dew Pointing Unit of South Pars Gas Refinery,” Journal of Chemical and Petroleum Engineering, University of Tehran, vol. 45, no. 2, pp. 109-116, 2011.
[11] J. Haydary and T. Pavlík, “STEADY-STATE AND DYNAMIC SIMULATION OF CRUDE OIL DISTILLATION USING ASPEN PLUS AND ASPEN DYNAMICS,” Petroleum & Coal, vol. 51, no. 2, pp. 100-109, 2009.
[12] S. J. Qin and T. A. Badgwell, “A survey of industrial model predictive control technology,” Control Engineering Practice, vol. 11, p. 733–764, 2002.
[13] seas.upenn, “Model Predictive Control : Basic Concept,” 2015. [Online]. Available: http://www.seas.upenn.edu/~ese680/papers/IntroductionMPC.pdf. [Accessed 28 December 2017].
[14] D. E. Seborg, D. A. Mellichamp, T. F. Edgar and F. J. Doyle III, Process Dynamics and Control, Chichester, United Kingdom: John Wiley & Sons, 2010.
[15] J. Carey, B. v. Kuiken, C. Longcore and A. Yeung, “RGA,” 1 November 2007. [Online]. Available: https://controls.engin.umich.edu/wiki/index.php/RGA. [Accessed 28 December 2015].
[16] Aspen Physical Property System, Physical property methods and models, Aspen Technology, 2006.
[17] Asprion N. & Kaibel G. (2010). Dividing wall columns: Fundamentals and recent Advances. Chemical Engineering and Processing: Process Intensification, 49, 139-146.
64 ASPEN PLUS & ASPEN DYNAMICS
[18] Hernandez S. & Gabriel S. H. (2006) Thermodynamically equivalent distillation schemes to the Petlyuk column for ternary mixtures. Energy, 31, 2176-2183
[19] Hiller C., Buck C., Ehlers C., & Fieg G. (2010). Non-equilibrium stage modelling of Dividing wall columns and experimental validation. Heat & Mass Transfer, 46, 1209– 1220 (2010)
[19] Luyben, W.L (2006). Distillation Design and Control Using Aspen Simulation, John Wiley & Sons, New York.
[20] Luyben,W. L & I-LungChien.(2010). Design and control of distillation systems for separating azeotropes.1-94
[21] Serra, M., Espuna, A. &Puigjaner, L. (1999). Control and optimization of the divided wall column. Chemical Engineering and Processing, 38, 549-562 (1999)
[22] Stupin W. J., Lockhart, F. J. (1972). Thermally coupled distillation-a case history.Chem. Eng. Program, 68, 71-72.
[23]VanDiggelen R.C., Kiss A.A., & Heemink A.W. (2010). Comparison of Control Strategies for Dividing-Wall Columns. Industrial& Engineering Chemistry Research, 49, 288-307.
65 ASPEN PLUS & ASPEN DYNAMICS
12 Appendices
12.1 Appendix A – Aspen Plus & Aspen Dynamic Results
12.1.1 Feed Stream
TABLE 8 FEED STREAM
Feed stream name Flow Ibmol/hr CO2e Feed (MCH-TOL) 400 - PHENOL 1200 -
12.1.2 Product Stream
TABLE 9 PRODUCT STREAM
Product Flow Ibmol/hr CO2e MCH 196.557 - BOTTOMS 1396.554 -
66 ASPEN PLUS & ASPEN DYNAMICS
12.1.3 Results of process stream
TABLE 10 STEADY-STATE RESULTS
Units BOTTOMS Distillation MCH-TOL PHENOL Substream: MIXED Phase Liquid Liquid Liquid Liquid Component Mole Flow PHENOL Ibmol/hr 1197.0 2.9566 0 1200 MCH Ibmol/hr 3.44262 196.557 200 0 TOLUENE Ibmol/hr 199.514 0.486027 200 0 Component Mole Fraction PHENOL 0.855031 0.014783 0 1 MCH 0.002459 0.982787 0.5 0 TOLUENE 0.14251 0.002430 0.5 0 Mole Flow Ibmol/hr 1400 200 400 1200 Mass Flow kg/sec 16.5534 2.47241 4.7962 14.2297 Volume Flow cum/sec 0.0179509 0.00354 0.00650 0.01396
FIGURE 31 STEADY-STATE RESULTS
67 ASPEN PLUS & ASPEN DYNAMICS
TABLE 11 TEMPERATURE AND PRESSURE AT STEADY STATE (ALL 22 STAGES)
Stage Temperature Pressure Liquid from (Mole) Vapor from (Mole) K psia kmol/sec kmol/sec
1 378.156 16 0.226796 0 2 378.696 16.2 0.201098 0.226796 3 379.258 16.4 0.200359 0.226297 4 379.863 16.6 0.1992 0.225558 5 380.561 16.8 0.197177 0.2244 6 381.497 17 0.193049 0.222376 7 383.059 17.2 0.376702 0.218248 8 383.552 17.4 0.377121 0.250704 9 384.069 17.6 0.377463 0.251123 10 384.627 17.8 0.377673 0.251465 11 385.261 18 0.377643 0.251675 12 386.036 18.2 0.377337 0.251645 13 387.021 18.4 0.375537 0.251339 14 388.759 18.6 0.432995 0.249539 15 389.703 18.8 0.432513 0.256598 16 391.008 19 0.43149 0.256116 17 392.868 19.2 0.430196 0.255093 18 395.386 19.4 0.429532 0.253798 19 398.322 19.6 0.430054 0.253135 20 401.229 19.8 0.428782 0.253656 21 405.089 20 0.40279 0.252385 22 423.738 20.2 0.176397 0.226393
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FIGURE 32 TEMPERATURE OF INDIVIDUAL 22 STAGES
FIGURE 33 PRESSURE LEVEL OF INDIVIDUAL STAGES
69 ASPEN PLUS & ASPEN DYNAMICS
12.1.4 Sensitivity Analysis- Results
TABLE 12 SENSITIVITY ANALYSIS RESULTS
Number of Stages Phenol Flowrate (Kmol/sec) MCH Purity is Distillation (Kmol/sec) 1 0.100798304 0.977992866 2 0.117598022 0.980239477 3 0.134397739 0.981737994 4 0.151197457 0.982781379 5 0.167997174 0.983537641 6 0.184796891 0.984108571 7 0.201596609 0.984556588 8 0.218396326 0.984920877 9 0.235196044 0.985226733
10 0.251995761 0.985490305
12.1.5 Results column sizing
FIGURE 34 HYDRAULIC PLOT
TABLE 13 COLUMN INTERNAL SUMMARY
Value Units Number of trayed 20 Total height 12.192 Meter Total head loss 2.17506 Meter Total pressure drop 2.43 psia Number of sections 3 Number of diameter 3 Pressure of diameters psia
70 ASPEN PLUS & ASPEN DYNAMICS
TABLE 14 SELECTED COLUMN SUMMARY
Start Stage
End Stage
Diameter (meters)
Selection Height (meters)
Internals Type
Tray Type
Selection Pressure Drop (N/sqm)
% Approach to Flood
Limiting Stage
CS-1 2 7 2.85 3.66 TRAY SIEVE 4515 80.0006 7 CS-2 8 14 2.88 4.27 TRAY SIEVE 6118.6 80.0007 14 CS-3 15 21 2.87 4.27 TRAY SIEVE 6098.94 80.0007 15
12.1.6 Results optimization
TABLE 15 OPTIMIZATION RESULTS
Objective function value 0.60956059 Iteration count
Number of iterations on last outer loop 1 Total number of flowsheet passes 54 Number of flowsheet passes on last outer loop 4 Sampled variable Initial value Final value Units FW1 14.2297 14.2297 kg/sec FW2 4.7962 4.7962 kg/sec DW 2.47241 2.47241 kg/sec BW 16.5534 16.5534 kg/sec QR 9.18215𝑒𝑒6 9.18215𝑒𝑒6 watt
71 ASPEN PLUS & ASPEN DYNAMICS
FIGURE 35 TUNED BASIC LEVEL CONTROLLER
