This document presents a Petri net model for coordinating control of a batch dye production process. Petri nets are introduced as a mathematical tool for modeling discrete event systems like batch processes. A simple dye production example involving a reactor, two filter systems, and storage tanks is described. Condition/event (C/E) Petri nets are used to model the interactions between the reactor and filter systems during product transfer and liquid recycling. The model shows the discrete states and events of the process, such as starting/stopping transfer when levels reach certain points. This provides a formal representation of the coordination control problem in the dye production system.
SYSTEM IDENTIFICATION AND MODELING FOR INTERACTING AND NON-INTERACTING TANK S...ijistjournal
System identification from the experimental data plays a vital role for model based controller design. Derivation of process model from first principles is often difficult due to its complexity. The first stage in the development of any control and monitoring system is the identification and modeling of the system. Each model is developed within the context of a specific control problem. Thus, the need for a general system identification framework is warranted. The proposed framework should be able to adapt and emphasize different properties based on the control objective and the nature of the behavior of the system. Therefore, system identification has been a valuable tool in identifying the model of the system based on the input and output data for the design of the controller. The present work is concerned with the identification of transfer function models using statistical model identification, process reaction curve method, ARX model, genetic algorithm and modeling using neural network and fuzzy logic for interacting and non interacting tank process. The identification technique and modeling used is prone to parameter change & disturbance. The proposed methods are used for identifying the mathematical model and intelligent model of interacting and non interacting process from the real time experimental data.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Closed-loop step response for tuning PID fractional-order filter controllersISA Interchange
Analytical methods are usually applied for tuning fractional controllers. The present paper proposes an empirical method for tuning a new type of fractional controller known as PID-Fractional-Order-Filter (FOF-PID). Indeed, the setpoint overshoot method, initially introduced by Shamsuzzoha and Skogestad, has been adapted for tuning FOF-PID controller. Based on simulations for a range of first order with time delay processes, correlations have been derived to obtain PID-FOF controller parameters similar to those obtained by the Internal Model Control (IMC) tuning rule. The setpoint overshoot method requires only one closed-loop step response experiment using a proportional controller (P-controller). To highlight the potential of this method, simulation results have been compared with those obtained with the IMC method as well as other pertinent techniques. Various case studies have also been considered. The comparison has revealed that the proposed tuning method performs as good as the IMC. Moreover, it might offer a number of advantages over the IMC tuning rule. For instance, the parameters of the frac- tional controller are directly obtained from the setpoint closed-loop response data without the need of any model of the plant to be controlled.
Solving output control problems using Lyapunov gradient-velocity vector functionIJECEIAES
This paper describes a controller and observer parameter definition approach in one input-one output (closed-loop) control systems using Lyapunov gradient-velocity vector function. Construction of the vector function is based on the gradient nature of the control systems and the parity of the vector functions with the potential function from the theory of catastrophe. Investigation of the closed-loop control system’s stability and solution of the problem of controller (determining the coefficient of magnitude matrix) and observer (calculation of the matrix elements of the observing equipment) synthesis is based on the direct methods of Lyapunov. The approach allows to select parameters based on the requested characteristics of the system.
In this paper, block-oriented systems with linear parts based on Laguerre functions is used to
approximation of a cone crusher dynamics. Adaptive recursive least squares algorithm is used to
identification of Laguerre model. Various structures of Hammerstein, Wiener, Hammerstein-Wiener models
are tested and the MATLAB simulation results are compared. The mean square error is used for models
validation.It has been found that Hammerstein-Wiener with orthonormal basis functions improves the
quality of approximation plant dynamics. The mean square error for this model is 11% on average
throughout the considered range of the external disturbances amplitude. The analysis also showed that
Wiener model cannot provide sufficient approximation accuracy of the cone crusher dynamics. During the
process it is unstable due to the high sensitivity to disturbances on the output.The Hammerstein-Wiener
model will be used to the design nonlinear model predictive control application.
EFFECT OF TWO EXOSYSTEM STRUCTURES ON OUTPUT REGULATION OF THE RTAC SYSTEMijctcm
This paper presents results on the output regulation of a single-input multi-output (SIMO) rotationaltranslational actuator (RTAC) system. The results focus primarily on stability and robustness, which are
studied in light of the presence of externally generated exogenous input signals. Two exosystem types were
investigated and tested. Obtained results answers the question of asymptotic stabilization and tracking of a
desired trajectory in the presence of a dynamic exosystem. The results confirmed the working theory of
robust stabilization using output feedback techniques, borne out of differential-geometric observer design
principles. The utilized design showed good stability results which compares favourably with existing
works on RTAC stabilization.
SYSTEM IDENTIFICATION AND MODELING FOR INTERACTING AND NON-INTERACTING TANK S...ijistjournal
System identification from the experimental data plays a vital role for model based controller design. Derivation of process model from first principles is often difficult due to its complexity. The first stage in the development of any control and monitoring system is the identification and modeling of the system. Each model is developed within the context of a specific control problem. Thus, the need for a general system identification framework is warranted. The proposed framework should be able to adapt and emphasize different properties based on the control objective and the nature of the behavior of the system. Therefore, system identification has been a valuable tool in identifying the model of the system based on the input and output data for the design of the controller. The present work is concerned with the identification of transfer function models using statistical model identification, process reaction curve method, ARX model, genetic algorithm and modeling using neural network and fuzzy logic for interacting and non interacting tank process. The identification technique and modeling used is prone to parameter change & disturbance. The proposed methods are used for identifying the mathematical model and intelligent model of interacting and non interacting process from the real time experimental data.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Closed-loop step response for tuning PID fractional-order filter controllersISA Interchange
Analytical methods are usually applied for tuning fractional controllers. The present paper proposes an empirical method for tuning a new type of fractional controller known as PID-Fractional-Order-Filter (FOF-PID). Indeed, the setpoint overshoot method, initially introduced by Shamsuzzoha and Skogestad, has been adapted for tuning FOF-PID controller. Based on simulations for a range of first order with time delay processes, correlations have been derived to obtain PID-FOF controller parameters similar to those obtained by the Internal Model Control (IMC) tuning rule. The setpoint overshoot method requires only one closed-loop step response experiment using a proportional controller (P-controller). To highlight the potential of this method, simulation results have been compared with those obtained with the IMC method as well as other pertinent techniques. Various case studies have also been considered. The comparison has revealed that the proposed tuning method performs as good as the IMC. Moreover, it might offer a number of advantages over the IMC tuning rule. For instance, the parameters of the frac- tional controller are directly obtained from the setpoint closed-loop response data without the need of any model of the plant to be controlled.
Solving output control problems using Lyapunov gradient-velocity vector functionIJECEIAES
This paper describes a controller and observer parameter definition approach in one input-one output (closed-loop) control systems using Lyapunov gradient-velocity vector function. Construction of the vector function is based on the gradient nature of the control systems and the parity of the vector functions with the potential function from the theory of catastrophe. Investigation of the closed-loop control system’s stability and solution of the problem of controller (determining the coefficient of magnitude matrix) and observer (calculation of the matrix elements of the observing equipment) synthesis is based on the direct methods of Lyapunov. The approach allows to select parameters based on the requested characteristics of the system.
In this paper, block-oriented systems with linear parts based on Laguerre functions is used to
approximation of a cone crusher dynamics. Adaptive recursive least squares algorithm is used to
identification of Laguerre model. Various structures of Hammerstein, Wiener, Hammerstein-Wiener models
are tested and the MATLAB simulation results are compared. The mean square error is used for models
validation.It has been found that Hammerstein-Wiener with orthonormal basis functions improves the
quality of approximation plant dynamics. The mean square error for this model is 11% on average
throughout the considered range of the external disturbances amplitude. The analysis also showed that
Wiener model cannot provide sufficient approximation accuracy of the cone crusher dynamics. During the
process it is unstable due to the high sensitivity to disturbances on the output.The Hammerstein-Wiener
model will be used to the design nonlinear model predictive control application.
EFFECT OF TWO EXOSYSTEM STRUCTURES ON OUTPUT REGULATION OF THE RTAC SYSTEMijctcm
This paper presents results on the output regulation of a single-input multi-output (SIMO) rotationaltranslational actuator (RTAC) system. The results focus primarily on stability and robustness, which are
studied in light of the presence of externally generated exogenous input signals. Two exosystem types were
investigated and tested. Obtained results answers the question of asymptotic stabilization and tracking of a
desired trajectory in the presence of a dynamic exosystem. The results confirmed the working theory of
robust stabilization using output feedback techniques, borne out of differential-geometric observer design
principles. The utilized design showed good stability results which compares favourably with existing
works on RTAC stabilization.
Enhancing the Performance of An Industrial Boiler Using Fuzzy Logic ControllerIJMREMJournal
This paper focus on the performance of an industrial boiler using fuzzy logic controller. The parameter of the
various industrial boilers are subjected to the change due to change in the environment or atmosphere. These
parameter may be categorized as steam, pressure and temperature of the industrial boiler in use. In this paper
work, a strategy of fuzzy logic controller called fuzzy supervisory is used which generates set points for the
conventional controllers. This work also compared the performance of a boiler evaporator system when the
system is controlled by a traditional proportional integral derivatives type strategy and when the system is
controlled using fuzzy logic blocs to provide set point for it. The main change consists of representing only the
behaviour of the drum evaporator system having a partial model of the combustion process with a simplified
combustion control system and a three element boiler feed water receives a supervisory signal that comes from
fuzzy logic to improve the performance of the overall control system.
Enhancing the Performance of An Industrial Boiler Using Fuzzy Logic ControllerIJMREMJournal
This paper focus on the performance of an industrial boiler using fuzzy logic controller. The parameter of the
various industrial boilers are subjected to the change due to change in the environment or atmosphere. These
parameter may be categorized as steam, pressure and temperature of the industrial boiler in use. In this paper
work, a strategy of fuzzy logic controller called fuzzy supervisory is used which generates set points for the
conventional controllers. This work also compared the performance of a boiler evaporator system when the
system is controlled by a traditional proportional integral derivatives type strategy and when the system is
controlled using fuzzy logic blocs to provide set point for it. The main change consists of representing only the
behaviour of the drum evaporator system having a partial model of the combustion process with a simplified
combustion control system and a three element boiler feed water receives a supervisory signal that comes from
fuzzy logic to improve the performance of the overall control system.
SYSTEM IDENTIFICATION AND MODELING FOR INTERACTING AND NON-INTERACTING TANK S...ijistjournal
System identification from the experimental data plays a vital role for model based controller design. Derivation of process model from first principles is often difficult due to its complexity. The first stage in the development of any control and monitoring system is the identification and modeling of the system. Each model is developed within the context of a specific control problem. Thus, the need for a general system identification framework is warranted. The proposed framework should be able to adapt and emphasize different properties based on the control objective and the nature of the behavior of the system. Therefore, system identification has been a valuable tool in identifying the model of the system based on the input and output data for the design of the controller. The present work is concerned with the identification of transfer function models using statistical model identification, process reaction curve method, ARX model, genetic algorithm and modeling using neural network and fuzzy logic for interacting and non interacting tank process. The identification technique and modeling used is prone to parameter change & disturbance. The proposed methods are used for identifying the mathematical model and intelligent model of interacting and non interacting process from the real time experimental data.
Analysis and Modeling of PID and MRAC Controllers for a Quadruple Tank System...dbpublications
Multivariable systems exhibit complex dynamics because of the interactions between input variables and output variables. In this paper an approach to design auto tuned decentralized PI controller using ideal decoupler and adaptive techniques for controlling a class of multivariable process with a transmission zero. By using decoupler, the MIMO system is transformed into two SISO systems. The controller parameters were adjusted using the Model Reference Adaptive reference Control. In recent process industries, PID and MRAC are the two widely accepted control strategies, where PID is used at regulatory level control and MRAC at supervisory level control. In this project, LabVIEW is used to simulate the PID with Decoupler and MRAC separately and analyze their performance based on steady state error tracking and overshoot.
IMC Based Fractional Order Controller for Three Interacting Tank ProcessTELKOMNIKA JOURNAL
In model based control, performance of the controlled plant considerably depends on the
accuracy of real plant being modelled. In present work, an attempt has been made to design Internal
Model Control (IMC), for three interacting tank process for liquid level control. To avoid complexities in
controller design, the third order three interacting tank process is modelled to First Order Plus Dead Time
(FOPDT) model. Exploiting the admirable features of Fractional Calculus, the higher order model is also
modelled to Fractional Order First Order Plus Dead Time (FO-FOPDT) model, which further reduces the
modelling error. Moving to control section, different IMC schemes have been proposed based on the order
of filter. Various simulations have been performed to show the greatness of Fractional order modelled
system & fractional order filters over conventional integer order modelled system & integer order filters
respectively. Both for parameters estimation of reduced order model and filter tuning, Genetic Algorithm
(GA) is being applied.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Metamodel-based Optimization of a PID Controller Parameters for a Coupled-tan...TELKOMNIKA JOURNAL
Liquid flow and level control are essential requirements in various industries, such as paper
manufacturing, petrochemical industries, waste management, and others. Controlling the liquids flow and
levels in such industries is challenging due to the existence of nonlinearity and modeling uncertainties of
the plants. This paper presents a method to control the liquid level in a second tank of a coupled-tank plant
through variable manipulation of a water pump in the first tank. The optimum controller parameters of this
plant are calculated using radial basis function neural network metamodel. A time-varying nonlinear
dynamic model is developed and the corresponding linearized perturbation models are derived from the
nonlinear model. The performance of the developed optimized controller using metamodeling is compared
with the original large space design. In addition, linearized perturbation models are derived from the
nonlinear dynamic model with time-varying parameters.
Comparing the effect of PI and I controllers on a four tank processpaperpublications3
Abstract:In industrial control systems the liquid level is carrying its significance as the control action for level control in tanks containing different chemicals or mixtures is essential for further control linking set points. In this paper we will investigate the effect of PI and I controllers on a four tank process and will compare this controllers together. We want to know how much is the effect of, I and PI controllers on a process control. For more clarification we simulate the effect of PI and I controllers on this system.
Integrated application of synergetic approach for enhancing intelligent steam...IJECEIAES
This article focuses on the integrated application of the synergetic approach to enhance the quality of intelligent steam generator control systems. By combining various techniques such as model-based control, adaptive control, and artificial intelligence, an efficient and flexible control system can be developed. Model-based control utilizes mathematical models of steam generators to formulate control algorithms and predict system behavior. Adaptive control enables the system to adapt to changing conditions by adjusting control parameters based on real-time measurements. Artificial intelligence techniques, including neural networks and genetic algorithms, facilitate learning, optimization, and data-driven decision-making processes. The objectives of this research are to investigate the benefits of the synergetic approach in steam generator control, including improved steam generation efficiency, optimized energy consumption, enhanced system stability and reliability, and adaptability to varying operating conditions and disturbances. The findings and conclusions of this study are expected to provide valuable insights for engineers, researchers, and professionals involved in the design and implementation of intelligent steam generator control systems. By integrating the synergetic approach, substantial enhancements in control quality can be achieved, leading to optimal operation and maximum efficiency of power plants.
DESIGN, IMPLEMENTATION, AND REAL-TIME SIMULATION OF A CONTROLLER-BASED DECOUP...IAEME Publication
In this paper, dynamic decoupling control design strategies for the MIMO Continuous Stirred Tank Reactor (CSTR) process characterised by nonlinearities, loop interaction and the potentially unstable dynamics, are presented. Simulations of the behavior of the closed loop decoupled system are performed in Matlab/Simulink. Software transformation technique is proposed to build a real-time module of the developed in Matlab/Simulink environment software modules and to transfer it to the real-time environment of TwinCAT 3.1 software of the Beckhoff PLC. The simulation results from the investigations done in Simulink and TwinCAT 3.1 software platforms have shown the suitability and the potentials of the method for design of the decoupling controller and of merging the Matlab/Simulink control function blocks into the TwinCAT 3.1 function blocks in real-time. The merits derived from such integration imply that the existing software and its components can be re-used. The paper contributes to implementation of the industrial requirements for portability and interoperability of the PLC software.
Comparing the effect of PI and I controllers on a four tank processpaperpublications3
Abstract:In industrial control systems the liquid level is carrying its significance as the control action for level control in tanks containing different chemicals or mixtures is essential for further control linking set points. In this paper we will investigate the effect of PI and I controllers on a four tank process and will compare this controllers together. We want to know how much is the effect of, I and PI controllers on a process control. For more clarification we simulate the effect of PI and I controllers on this system.
Keywords:PI controller, I controller, four tank process, Level control, Coupled water tanks.
New calculation of thetray numbers for Debutanizer Tower in BIPCinventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
New calculation of thetray numbers for Debutanizer Tower in BIPCinventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
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Enhancing the Performance of An Industrial Boiler Using Fuzzy Logic ControllerIJMREMJournal
This paper focus on the performance of an industrial boiler using fuzzy logic controller. The parameter of the
various industrial boilers are subjected to the change due to change in the environment or atmosphere. These
parameter may be categorized as steam, pressure and temperature of the industrial boiler in use. In this paper
work, a strategy of fuzzy logic controller called fuzzy supervisory is used which generates set points for the
conventional controllers. This work also compared the performance of a boiler evaporator system when the
system is controlled by a traditional proportional integral derivatives type strategy and when the system is
controlled using fuzzy logic blocs to provide set point for it. The main change consists of representing only the
behaviour of the drum evaporator system having a partial model of the combustion process with a simplified
combustion control system and a three element boiler feed water receives a supervisory signal that comes from
fuzzy logic to improve the performance of the overall control system.
Enhancing the Performance of An Industrial Boiler Using Fuzzy Logic ControllerIJMREMJournal
This paper focus on the performance of an industrial boiler using fuzzy logic controller. The parameter of the
various industrial boilers are subjected to the change due to change in the environment or atmosphere. These
parameter may be categorized as steam, pressure and temperature of the industrial boiler in use. In this paper
work, a strategy of fuzzy logic controller called fuzzy supervisory is used which generates set points for the
conventional controllers. This work also compared the performance of a boiler evaporator system when the
system is controlled by a traditional proportional integral derivatives type strategy and when the system is
controlled using fuzzy logic blocs to provide set point for it. The main change consists of representing only the
behaviour of the drum evaporator system having a partial model of the combustion process with a simplified
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fuzzy logic to improve the performance of the overall control system.
SYSTEM IDENTIFICATION AND MODELING FOR INTERACTING AND NON-INTERACTING TANK S...ijistjournal
System identification from the experimental data plays a vital role for model based controller design. Derivation of process model from first principles is often difficult due to its complexity. The first stage in the development of any control and monitoring system is the identification and modeling of the system. Each model is developed within the context of a specific control problem. Thus, the need for a general system identification framework is warranted. The proposed framework should be able to adapt and emphasize different properties based on the control objective and the nature of the behavior of the system. Therefore, system identification has been a valuable tool in identifying the model of the system based on the input and output data for the design of the controller. The present work is concerned with the identification of transfer function models using statistical model identification, process reaction curve method, ARX model, genetic algorithm and modeling using neural network and fuzzy logic for interacting and non interacting tank process. The identification technique and modeling used is prone to parameter change & disturbance. The proposed methods are used for identifying the mathematical model and intelligent model of interacting and non interacting process from the real time experimental data.
Analysis and Modeling of PID and MRAC Controllers for a Quadruple Tank System...dbpublications
Multivariable systems exhibit complex dynamics because of the interactions between input variables and output variables. In this paper an approach to design auto tuned decentralized PI controller using ideal decoupler and adaptive techniques for controlling a class of multivariable process with a transmission zero. By using decoupler, the MIMO system is transformed into two SISO systems. The controller parameters were adjusted using the Model Reference Adaptive reference Control. In recent process industries, PID and MRAC are the two widely accepted control strategies, where PID is used at regulatory level control and MRAC at supervisory level control. In this project, LabVIEW is used to simulate the PID with Decoupler and MRAC separately and analyze their performance based on steady state error tracking and overshoot.
IMC Based Fractional Order Controller for Three Interacting Tank ProcessTELKOMNIKA JOURNAL
In model based control, performance of the controlled plant considerably depends on the
accuracy of real plant being modelled. In present work, an attempt has been made to design Internal
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controller design, the third order three interacting tank process is modelled to First Order Plus Dead Time
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modelling error. Moving to control section, different IMC schemes have been proposed based on the order
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system & fractional order filters over conventional integer order modelled system & integer order filters
respectively. Both for parameters estimation of reduced order model and filter tuning, Genetic Algorithm
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The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Metamodel-based Optimization of a PID Controller Parameters for a Coupled-tan...TELKOMNIKA JOURNAL
Liquid flow and level control are essential requirements in various industries, such as paper
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the plants. This paper presents a method to control the liquid level in a second tank of a coupled-tank plant
through variable manipulation of a water pump in the first tank. The optimum controller parameters of this
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nonlinear model. The performance of the developed optimized controller using metamodeling is compared
with the original large space design. In addition, linearized perturbation models are derived from the
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Abstract:In industrial control systems the liquid level is carrying its significance as the control action for level control in tanks containing different chemicals or mixtures is essential for further control linking set points. In this paper we will investigate the effect of PI and I controllers on a four tank process and will compare this controllers together. We want to know how much is the effect of, I and PI controllers on a process control. For more clarification we simulate the effect of PI and I controllers on this system.
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International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
New calculation of thetray numbers for Debutanizer Tower in BIPCinventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
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Coordination control modelling in batch production systems by means of petri nets
1. Compurers them. Engng, Vol. 16, No. 1, pp. I-10, 1992
Printedin Great Britain.All rights reserved
009%1354/92 $5.00 + 0.00
copyrightQ 1992 FergamonPressplc
COORDINATION CONTROL MODELLING IN BATCH
PRODUCTION SYSTEMS BY MEANS OF PETRI NETS
H.-M. HANISCH
Department of Chemical Engineering, Technical University Leuna-Me-burg,
Otto-Nuschke-StraBe, D-4200 Merseburg. Fed. Rep. Germany
(Received 7May 1991;finai revision received II September 199I; receivedforpublication 25 September 1991)
Absiract-The paper presents an introduction to a mathematical method for modelling discrete
coordination control in batch production systems.
The process to be controlled is modelled usingPetrinet theory,a tool that is suitablefor the design
of discrete supervisory controllers, as well as for performance evaluation of the controlled process. As an
illustration, simple condition/event systems and higher Petri nets are applied to model a dye-stuff
production pro&s.
1. INTRODUCTTON
Most of the modern design methods for automatic
control systems are based on process models. Batch
production systems are not an exception, only in that
the models are different from those used to describe
continuous processes mostly because of the appreci-
able amount of discrete control actions. These usually
simple binary switching operations give rise to the
discrete control problem the complexity of which
increases with the number of logical interactions,
such as interlocked flows of material, energy and
shared resources as is typical for multipurpose batch
production systems. In order to achieve the desired
performance of entire production systems precise
models describing the discrete system behaviour on
this level are needed.
Up to now, coordination control tasks of this kind
have been mainly described in a non-formal, textual
form, which in most cases is extensive and rarely
unambiguous. Such a textual representation is by no
means sufficient, neither for an exact analysis of
interactions in a complex process nor for a computer-
aided formulation of the control problems. In order
to solve these problems, a general mathematical
formulation is called for which allows to analyze the
interactions of the different process units and which
is suitable for the design of discrete supervisory
controller units.
2. NEED FOR COORDINATION CONTROL
Objectives, control tasks and models for auto-
mation of complex multipurpose batch production
systems can be arranged in a hierarchical control
system such as is shown in Fig. 1.
The present paper deals with control tasks on
the synchronization and coordination level of batch
processes. As a rule, batch process systems are com-
posed of several process units working in parallel
coupled by flows of mass and energy. The coupling
pattern is time-dependent and many of the control
actions are of a discrete nature such as opening and
closing of control valves or starting and stopping of
pumps etc.
Example
Figure 2 shows an example of a batch production
system of an organic dye. The solid main product,
produced in reactor 1, is dispersed in a liquid phase.
Two filter subsystems (2,3) and (4, 5) are provided
for the separation of solid and liquid phases. The
liquid phase is stored in the storage tanks (3,5) to be
recycled in a subsequent batch, the solid is discharged
from the filters after completion of the washing
process.
Most of the time, reactor and filter systems work
independently, i.e. in parallel. On two occasions,
however, these units interact which are:
1. At the completion of the reaction process the
product must be discharged into one of the filter
systems.
2. After the filtration is completed the liquid phase
is recycled and reused in a subsequent batch.
Further interactions between process units may
arise from limitations of jointly used resources such
as:
-available metering tanks
-capacity of utilities (steam, electric power etc.)
-capacity of reactors and storage tanks
-available trained operators.
Example
In our example, the goal of coordination control is
to guarantee that:
1.
1
At the end of the reaction the whole batch is
discharged from the reactor into only one of the
filter systems.
2. H.-M. HANISCH
I
Saqurncc Control Lava1
(81)
I
several procrrrer
In a multipurpoma plant
(for instance dye pro-
duction)
several procarr unit6
forming a batab procemm
syrtem
ring10 batch preemss,
procerr unit
(for Inetancea drying,
reaction)
logical group8 of one ar
more procees variable6
(for inrtmce~ prerrure.
temperature, conarntration)
I
Meamarimg and Actuating
Level (HAL)
I
I I I I
I BATCH PROCESS
I
Fig. 1. Hierarchicalsystem.
2. At the beginning of a batch the liquid phase 1982; Starke, 1980). This method and its derivatives
from only one filter system is recycled. gained a leading position mostly because they are
characterized by a well-balanced relation between
3. DISCRETE-EVENT SYSTEMS MODELLINC WITH
graphical representation and mathematical abstrac-
PETRI NETS tion (Helms et al., 1989). Petri nets have no consider-
3.1. Modelerling capabilities of Petri nets
able advantage over other related modelhng methods
(automata-graphs and -tables, BOOLEan equations,
In the last two decades, Petri nets proved to be a sequential flowcharts etc.) when modelling pure
powerful tool for describing and analyzing concur- sequential operations as they are typical for the
rent processes (Brauer, 1980; Peterson, 1981; Reisig, control of single-unit batch processes. As part of a
f ... Reactor
24 ._. Filters
qS... Storage tanks
for liquid @use
solid purge of liquid
product phW9
Fig. 2. Flowsheet.
3. Coordination control modelling in batch production systems 3
ordinary Petri nets
(token weight of all
arcs -fI
higher Petri nets
Fig. 3. Classes of Petri nets.
model hierarchy, Petri nets are more useful for mod-
elling coordination problems on the upper control
levels and less useful for describing the details of the
sequential processes. Hence, they are usually applied
to control tasks on the synchronization and coordi-
nation level as described in Section 2.
Commonly, Petri nets are thought to represent
condition/event systems (C/E systems), but in fact,
C/E systems are only the simplest class of Petri
nets.
Figure 3 illustrates “classical” Petri nets. Beyond
these, a number of modifications and extensions
are known. This paper utilizes only classical Petri
nets.
The modelling capabilities of the various classes of
Petri nets differ considerably:
-C/E systems may be applied to model simple
interactions in small systems
-Place/transition nets (P/T nets) are suitable
for the modelling of medium-s&d systems of
medium complexity
-Predicate/transition nets (Pr/T nets) are used for
modelling complex interactions in large-scale
systems such as multiproduct or multipurpose
batch plants.
The general strategy of designing discrete controllers
handling the coordination control problem of batch
processes using Petri nets can be illustrated by dis-
cussing simple C/E systems. Hence, this paper deals
mainly with C/E systems (Sections 3 and 4). Section
5 discusses the modelling capabilities of higher classes
of Petri nets.
3.2. Formal properties of C/E systems
Formally, Petri nets are directed graphs with two
kinds of nodes.
Conditions are represented by circles and may be
true or false. In the graphical representation, the
circle carries a token (usually a black dot) if the
condition is true and no token otherwise. A condition
carrying a token is called a marked condition.
Events are represented by bars. Activities are
a special kind of event and are represented by
rectangles.
The net structure consists of directed arcs. They
link conditions with events or events with conditions,
but never two conditions or two events. Arcs are
represented by arrows.
Figure 4 shows an event and several conditions.
Relative to event e, the set of conditions can be
divided into preconditions of e (arrow from the
condition to the event) and postconditions of e
(arrow from the event to the condition). Post-
conditions of an event may be preconditions of
another event and so on. The structure of a
net represents the causal relations of the modelled
system. These relations are time-independent (static}.
Dynamic changes of the states are represented by the
flow of tokens in the net. The net may contain several
tokens in several conditions, called the marking of the
net. The modelling of concurrent processes is possible
by the existence of more than one token.
The rules for changing the marking depend on the
net class. C/E systems are controlled by a very simple
rule, higher Petri nets by more complicated ones.
Generally, the rule for changing the marking consists
of two parts.
EnabIing rule. An event e is enabled if all pre-
conditions of e are marked and all postconditions
of e are not marked (Fig. 4). Only an enabled event
may fire.
Firing rule. By firing e all preconditions of e
lose their tokens and all postconditions of e receive
one token. Figure 4 illustrates the firing of e. Firing
ireconditions pohconditions
ofe of 8
e is enabled e fires marking after firing of e
Fig. 4. Firing of an event.
4. 4 H.-M. HANIXH
t
Fig. 5. Couplingof two tanks.
an event may change the number of tokens in the The process information in C/E models is given as
net. follows:
The firing of an event does not take any time.
Ideally, the firing occurs exactly at a particular
instant in time. Activities are an exception to this rule.
They represent sequential processes that are not
described on this modelling level. Hence, the ex-
ecution of activities may require some finite time. The
details of an activity are modelled using separate
models and are controlled on the sequential control
level (cf. Fig. 1).
1. Conditions are labelled with ranges of values of
process variables denoting discrete states of the
process units.
Each process state requires certain values of
control variables that must be realized by the
controller. Such values of control variables are
also assigned to the conditions.
4. APPLICATION OF C/E SYSTEMS
4.1. Process information in C/E systems
Originally, the formal mechanism of Petri nets is
free of any semantics. Thus, if Petri nets are to be
applied to particular processes, the elements of the
Petri net must be completed with additional process
information describing the relations between the
formal Petri net model and the particular process.
Only the combination of both the Petri net and the
process information represents a complete descrip-
tion of the process.
Example-Figure 5 shows the coupling of
two tanks (B,, B2) through a mass flow. The
state description is given in Table 1. Condition
c, denotes a certain state of B, (B, is full) which
determines the values of two binary control
variables (V, closed, V, closed).
Condition c, represents a state of B2 (Bz is
being discharged). For this I’, must be open.
These models comprise two major pieces of infor-
mation:
The marking of the net denotes the current state
of the modelled system.
Example-The marking of the net in Fig. 5
shows that B, is full and B2 is being discharged.
1. The performance of the process (desired mode
of operation).
2. The control operations necessary to achieve the
performance defined in 1.
Evenrs denote state transitions of the modelled
system. State transitions are caused by switching
binary control variables. Usually, binary con-
trol variables must be switched by the controller
when process variables reach predetermined
limits.
It is assumed that the process (controlled by the
controllers of the PL and SL-see Fig. 1) guarantees
the execution of the control operations imposed by
the discrete supervisory controller.
Table 1. Process information of the net
It is important to recognize that the combination
of the two parts, namely the performance of the
process and the corresponding control operations,
do not describe the discrete controller being used to
supervise the plant but that they model the process as
a discrete event system.
model of Fig. 5
Process variables Control variables
=I L, = kn..x v, ( v, closed
f2 0 -c L2-z L2m.r V3 open
c3 0 -c L, < =hnlx V, closed, V, open
c4 O-=La-=Lz,, V, closed
fJ L, = 0 V,, I’, closed
% I? = Lnax v, closed
e, & = 0 is reached close v,, open v,
e, L, = 0 is reached close v,
5. Coordinationcontrol moclellingin batch productionsystems
Hence, events are labelled with limits of pro-
cess variables and the corresponding switching
operations of control variables.
I
4. The structure of the net represents the causal
relations between the states (conditions) and the
state transitions (events) of process units. The
preconditions and postconditions of an event
are the causes and the results of the state
transition described by the event.
Example-Event ez in Fig. 5 denotes the
termination of the mass transfer from B, to B2_
Termination requires that the mass transfer
is actually taking place (denoted by the pre-
conditions cj and c, of e2) at that particular
moment. The result of the termination, when
the level in B, reaches zero, is described by the
postconditions of e,: B, is empty (cj) and B, is
full (c.&
5. The change of the marking according to the
enabling and the firing rules represents the
change of the current state of the modelled
system.
Example-With the marking given in Fig. 5,
event e, is enabled. Event e, occurs at the
moment when the level in B2 reaches zero. Now
V, is opened, and the state of the system changes
(B, is being discharged, B2 is being filled). This
state transition is represented in the model by
the change of marking. According to the firing
rule, c, and c, lose their tokens, and cj and c,
receive tokens. The new marking represents the
new state.
4.2. Application to a dye production process
Example-Event e, in Fig. 5 indicates that
the discharge of B2 is completed when the level
of B2 reaches zero. Consequently V, must be
closed and V, opened thus starting the transfer
of the content of B, to B2.
Event e2 describes the switching operation
that stops the mass transfer. This operation
must be performed when the level in B, reaches
zero. Then V, must be closed.
In this section, a Petri net model of the system
shown in Fig. 2 is developed.
The process information, given in Tables 2-4, is of
textual nature only for the sake of a simple example,
the conditions and events describing the operation of
the sample process neither include the valves nor the
pump. Normally, this description should also include
this type of information as was previously mentioned
and is shown in Table 1.
Figure 6 shows a very simple Petri net which
describes the interactions between the reactor (left
subnet) and the filter system (2, 3) (right subnet).
The product transfer from the reactor into the filter
Reacror I Filrer system I 2,3 1
Fig. 6. Simple C/E system model.
system and the recycling of the liquid phase are
represented in a clear and simple way. The model
assumes the following sequence of operations for
discharging the reactor.
The product transfer is started by opening the
bottom valve of the reactor (es). Simultaneously, the
filter system (2,3) starts to separate the liquid phase
from the solid product (en). The liquid phase is
stored for recycling (No. 3 in Fig. 2). The product
transfer lasts until the reactor level reaches zero (es).
The separation process lasts until all product has
been separated (e,). In fact, no causal dependence
between e, and e, exists. Therefore, the pre- and
postconditions of e6 and e, are disjoint.
Figure 7 shows the same model with the sequential
processes being replaced by activities and events er
and e,, merged in en (see Table 2). Note that the
recycling of the liquid phase is a process that takes
time, which is the reason this process is represented
as an activity.
Lastly, the filter system (4,5) must be integrated
into the model. The subnet describing this part has
the same structure as the one describing the filter
system (2, 3). Figure 8 shows the net including both
filter systems. The current state of the system, as
indicated by the marking of the net of Fig. 8, shows
that the reactor and filter system (2,3) are empty
(c,, clz) in this state, whereas filter system (4, 5) is
waiting for the recycling of the liquid phase from a
preceding batch (c13).
The marking expresses a state that represents a
desired mode of operation. All other states can be
computed by successive firing of enabled events or
activities (see Section 6). With the given marking, a,
6. H.-M. HANIXH
Cl
II
1 a1 j
I
I
I =7
Reactor
I Filter system 12,3)
Fig. 7. Introductionof activities.
can fire, afterwards czror a, (concurrently), e,r or e,5
(exclusively) and so on.
5. APPLICATIONOF HIGHERPETRINETS
5.1. Place/transition nets (P/T nets)
Place/transition nets are C/E systems with a few
added features.
Conditions, called places in P/T nets, may carry
more than one token. Each place has an assigned
capacity which is the maximum number of tokens the
particular place may carry.
Graphically, a place is again represented by a
circle. As a general rule, it is assumed that if no
capacity is assigned to a place, the capacity of this
place is one, which corresponds to the definition used
in C/E systems.
Arcs of a P/T net may be weighted with a non-
negative integer value called a token weight or multi-
plicity. The token weight denotes how many tokens
flow across the arc when the corresponding event
(now culled trnnsition) fires. In the graphical rep-
resentation the token weight is assigned to the corre-
sponding arrow. If no token weight is assigned to an
arc, then it has the value one, which again corre-
sponds to the definition used in C/E systems.
A transition t is enabled if:
-the marking of all preplaces oft is larger than or
equal to the token weight of the corresponding
arc
-the capacities of all postplaces of t are large
enough to receive a number of tokens given by
the token weight of the corresponding arc.
The change of the marking by firing t is shown in
Fig. 9.
An exact definition of P/T nets is given in
Reisig (1982) and Starke (1980) and their use for
modelling technological processes by adding process
information is described in Han&h (1987a, b).
Application. For the purpose of illustrating the
modelling capabilities of P/T nets, the production
system of Fig. 2 is modified by assuming that the
product is discharged only when five batches have
been separated in the filter system.
Figure 10 shows the corresponding net. The pro-
cess information is given in Table 3. The number of
tokens on p10 and P,~ express how many batches have
already been separated by the filter system, whereas
the number of tokens on p,* and pks denote how many
batches are still to be separated before the discharge
of the product can be performed. The activities a, and
a, model the discharge of the product as above.
However, they fire only if five tokens have accumu-
lated on p,,, or P,~.
By firing a3 or a, all the tokens are removed from
plo or p14 and p12 or p15 receives five tokens. This
means that after discharging the product, five batches
must be separated before the product is discharged
again. Thus, the operational requirements are exactly
met.
Note that p,, and p18 are necessary in order to
guarantee the required behaviour of the system.
Table 2. Process informationof the net model of Figs 6-8
Reactor is empty
Reactor is being filled with liquid phase
Reactor contains liquid phase
Reaction
Reactor contains product
Reactor is being discharged
Product is being separated by filter system (2.3)
Filter system (2, 3) contains liquid phase to be recycled
Discharge of liquid phase from fitter system (2.3)
Filter system (2,3) does not contain liquid phase
Discharge of the product from filter system (2,3)
Filter system (2.3) is empty
Filter system (4, 5) contains liquid phase to bc recycled
Filter system (4,5) does not contain liquid phase
Filter system (4.5) is empty
Product is being separated by filter system (4.5)
Filling of the reactor with liquid phase starts
Fillina of the reactor with liauid uhase ends
Rexcon starts
_ .
Reaction ends
Discharge of the reactor starts
DiSChUge of the reactor ends
Separation by filter system (2.3) ends
Discharge of liquid phase from filter system (2,3) starts
Discharge of liquid phase from filter system (2.3) ends
Discharge of product from filter system (2, 3) starts
Discharge of product from filter system (2, 3) ends
Separation by filter system (2,3) starts
Discharge of the reactor and separation by filter system (2.3)
start
Discharge of the reactor and separation by filter system (4.5)
start
Separation by filter system (4,5) ends
Transfer of liquid phase from filter system (2,3) into the
reactor
Reaction
Discharge of product from filter system (2. 3)
Transfer of liquid phase from filter system (4, 5) into the
reactor
Discharge of product from filter system (4. 5)
7. Coordination control modelling in batch production systems 7
I ‘6 I c7
I I
Fittersystem (4,s) 1 Reactor I Fittersystem f 2,31
Fig. 8. Complete C/E system model.
Otherwise, a batch could be discharged into a filter
system that has just finished separating a preceding
batch and is waiting for the recycling of the liquid
phase (ps or pII are marked). This would cause an
overflow of the storage tanks.
5.2. Predicate/transition nets (Pr/T nets)
Pr/T nets are developed from P/T nets by adding
the concept of individual tokens. The tokens in Pr/T
nets are no longer indistinguishable black dots,
but have their own individuality and are therefore
distinguishable.
Places are now calledpredicates and may carry one
or more individual tokens (again limited by a defined
capacity).
Arcs transfer one or more individual tokens (given
by the token weight). An inscription at the arc denotes
which types of tokens are transferred by the arc.
Transitions may be labelled with logical expressions.
The enabling and firing rules are similar to those of
P/T nets with the extension that the individual tokens
must be in agreement with the inscriptions at the arcs
and the logical expression at the transition must be
true.
2
Pr/T nets are defined in detail in Genrich and
Lautenbach (1981) and their use for modelling tech-
nological processes by adding process information is
described in Hanisch (1987a, b).
Application. Figure 11 (see also Table 4) gives a
very brief introduction to the use of Pr/T nets for the
modelling of batch processes. In Section 4, it has been
shown that the two subnets of the filter systems (2,3)
and (4, 5) have the same structure. The only differ-
ence is that these two subnets describe two different
filter systems. On the conceptual level of Pr/T nets,
these two subnets can be “folded” into one subnet as
shown in Fig. 11. The two filter systems are distin-
guished by the individual tokens “1” [for filter system
(2, 3)] and “2” [for filter system (4, 5)]. The arcs of
this subnet are labelled with the variable x, that may
assume the values “1” or “2”. This means that one
of the individual tokens “1” or “2” must flow across
the arc, but it does not denote which one of the
individual tokens it must be. That depends on the
marking of the prepredicates of a transition.
Note that the filter systems work independently
from each other. Therefore, the capacities of the
predicates p,, ps, p10 and p,2 must be 2 because it is
2
r is enobled t fires marking after firing of t
Fig. 9. Firing of a transition.
8. 8 H.-M. -H
Filter system (4.5) 1 Reactor 1 Filter system (2,3j
Fig. 10. P/T net model.
possible for both filter systems to perform the same
operation at the same time. Thus, the corresponding
predicate must be able to carry two tokens.
Note also that there is no need for introducing an
individual token for the reactor because there is only
one reactor. Hence, the left subnet is similar to those
in Fig. 7. The process information given in Table 4
also includes the variable x. It depends on the actual
value of x as to which filter system this description
refers to. The marking of the net in Fig. 11 describes
the following state [m( pi) denotes the marking of
predicate p,]:
-the reactor contains product [m(pS)]
-filter system (2, 3) is empty [m(p,,), x = l]
-filter system (4, 5) contains the liquid phase to be
recycled [m(p,), x = 21
On the conceptual level of Pr/T nets, very complex
and flexible systems can be modelled. For instance,
the inclusion of a third filter system into the system
would not cause a change of the net topology, only
a third individual token would have to be added. This
additional flexibility makes this net class an ideal tool
for modelling flexible batch production systems (mul-
tipurpose batch plants) with dynamic allocation of
resources. This tool has been used extensively by
developing several models of multipurpose batch
production systems. One of these models is described
in Hanisch (1985, 1987a.b). Most of the other work
has not yet been published.
6. MODEL VALIDATION
Petri nets are a descriptive method for modelling
and design. Human errors are common and thus
models and designs must be validated before they are
implemented in a real-life process.
Commonly occurring errors can be divided into
two, not completely disjoint, classes:
1. Incorrect specification of the desired process
performance (technological errors).
2. Incorrect transformation of the technological
requirements into the formal Petri net model
(formal, logical errors).
It is one of the greatest advantages of Petri net theory
that it provides a variety of possibilities for proving
the correctness or detecting the incorrectness of a
given net. The most simple way to detect errors of the
first class is simulating the model by successive firing
Table 3. Process information of the net model of Fig. IO
m(P,o) Number of batches separated by filter system (2,3)
m(Pll) Number of batches to be separated by filter system (2.3) before the product is discharged
“(P&4) Number of batches separated by filter system (4, 5)
MP,J) Number of batches to be separated by filter system (4,s) before the product is discharged
PI7 Filter system (4, 5) does not contain liquid phase
PI8 Filter system (2, 3) does not contain liquid phase
P,.PI.Ps.P6.Pl.Ps.P13,P~6 are equal to the conditions c, of Table 2
f6>f7rG3, ‘15. 116are equal to the events c, of Table 2
a-a< are eoual to the activities (I. of Table 2
m(p,) denotes the number of tokens in pi
9. Coordinationcontrol modellingin batch productionsystems 9
25P3
. 02
Reach r Filter system (2,3 1 -a
Filter system 14,s 1 -0
Fig. 11. Pr/T net model.
enabled events or transitions and testing whether the
behaviour of the model corresponds to the desired
one or not.
A universal way to detect all errors of the second
class is to compute the reachability graph of the net.
The reachability graph contains all possible states
and all state transitions of the system [see Starke
(1990a)l.
Based on the reachability graph, all properties
known from Petri net theory such as:
-1iveness
-deadlocks
-conflicts
and many others can be tested. In terms of Petri net
theory, this is called net analysis. A comprehensive
description of formal properties and analysis tech-
niques is given in Starke (1990a).
The marked net models of the sample process
are live. Hence, no deadlocks can occur. The nets in
Figs 8, 10 and 11 are not free of conflicts.
A detailed description of the technological mean-
ing of such properties is given in Hanisch (1985,
Tabk 4. Process information of tlte net model of Fig. II
Pl Product is king separated by 6lter system x
PS Filter system x contains liquid phase to be cycled
p10 Filter system x doea not contain liquid phase
p12 Filter system x is empty
t, Separation by filter system x ends
tll Discharge of the reactor and separation by filter system x start
01 Transfer of liquid phase from filter system x into the reactor
03 Discharge of product from filter system x
p, ,p3.pJ. ps are equal to the conditions c<of Table 2
ts is equal to event es of Table 2
+ is equal to activity al of Table 2
1987a, b). The special role of conflicts for coordi-
nation control is shown in Hanisch (1990a,b; 1991a).
Only very simple models can be sketched and tested
by hand, for more complex ones computer-based
tools are required. At present the author uses the
program system PAN (PSI, 1989) for net analysis,
PETRINET (Hanisch, 1989) for simulation and per-
formance evaluation of C/E systems and P/T nets
and ATNA (Starke, 19QOb)for optimization of timed
net models. The program system PETRINET is
implemented on several 16-bit personal computers
and was used in three large chemical companies for
system modelling and simulation.
7. CONCLUSIONS
Some of the applications of Petri nets to batch
processes are shown in Table 5. At present Petri net
models are used in three different types of application
which are:
Design of a supervisory conrrolier-The technologi-
cal description ensures that all information necessary
to design a supervisory controller is given in the
model. This goal can only be achieved by teaming up
the chemical engineer responsible for the process
operation and the control engineer who designs the
supervisory controller based on Petri net theory.
The key to the design is the description of the
production process on the appropriate level using
Petri nets. Once a sufficiently complete description
has been established, the supervisory controller can
be designed and realized as shown in K&rig and
Quack ( 1988).
Place Product
Table 5. Survey of applications
Aim of modellina Net class Remarks
Bitterfeld Organic dye products, Description of the desired C/E systems Survey given in Han&h (1986)
catalysts and other mode of operation P/T nets
Pr/T nets
Leune Resin Analysis of bottbnaks and P/T nets, timed (Thiemicke and Hanisch, 1991)
disturbances
BUla
Berlin
Polyvinylchloride
Coal electrodes
Optimization of start P/T nets, rimed Continuous separation of the product,
scheduling of batch reactors not published
Description of the desired Pr/T nets, timed Not published
mode of operation, analysis
of disturbances
Saalfeld Chocolate Throughput optimization P/T nets, timed Continuous process, discretely controlled
(Schmidt, 1990)
10. 10 H.-M. H
Performance evaluation-In order to apply Petri
nets for performance evaluation of the process, some
kinds of timed Petri nets must be used. Such concepts
are well known but they are beyond the scope of this
paper.
Timed Petri nets were applied to several batch
processes (see Table 5) to compute throughputs and
employment of limited resources. Some resultswill be
published in Thiemicke and Han&h (1991).
Process optimization-In the last 2yr, an analytic
method for determining optimal strategies for coordi-
nation control in batch processes based on timed
Petri nets was developed. The first simple approach
is published in Han&h (1990~). Improvements
and the mathematical formulation of the optimal
dynamic control problem are given in Hanisch
(199Ob,d,e). Based on these ideas, the analysis and
optimization tool ATNA (Starke, 1990b) was im-
plemented. A first experience of it is contained in
Hanisch (1991b).
Petri nets have shown to be an excellent tool
for improving the performance of batch processes.
The underlying mathematical theory of Petri nets
is well developed and provides powerful analytical
tools. This paper gives only an introduction with
the objective demonstrating the power of this
modelling and design method and the hope that more
and more chemical engineers will use this concept
to improve efficiency and also safety of batch
production systems.
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