- Email: [email protected]
Computer aided design of polymer reactors
Computers chem. Engng Vol.20, Suppl.. pp. $449-$454. 1996
Pergamon
S0098-1354(96)00085-3
Copyright© 1996ElsevierScience Ltd Printed in Great Britain. All rights reserved 0098-1354/96$15.00+0.~X)
COMPUTER AIDED DESIGN OF POLYMER REACTORS
A. PERTSINIDIS, E. PAPADOPOULOS, and C. KIPARISSIDES
Chemical Engineering Department and Chemical Process Engineering Research Institute Aristotle University of Thessaloniki P.O. Box 472, Greece
Abstract - This paper describes the development of CAD packages for high pressure polyethylene reactors. The overall goal of the software packages was to develop powerfull, flexible, adaptive design and predictive simulation tools that can follow and predict the operating conditions of a given high pressure polymer reactor in an accurate, prompt and comprehensive way. Their range of use includes the prediction in real time of the molecular properties of polymer produced in high pressure LDPE reactors, the estimation of control moves of key process variables as well as the prediction of the operational and product characteristics of alternative design options. Major points of consideration during the program development were the user friendliness of the input/output and the execution speed enabling its online use as a predictive tool. The functions of the packages have been built around a modular mathematical model of the reactor that includes the energy and material balances based on the leading moments of the Molecular Weight Distribution equations and supported by the physical and molecular properties evaluation equations.
INTRODUCTION The development of computer-aided design (CAD) tools has advanced dramatically in the past fourty years. Furthermore, the perspectives for the near future developments are even more promising (Evans, 1994). The traditionally set path for the development of general purpose simulation packages requires a CAD expertise as an added value to the mathematical models of unit operations and physical properties evaluation, established in the literature. This modeling and problem solving expertise encompasses a wide variety of interdisciplinary fields such as numerical analysis for the solution of differential and algebraic equations, mathematical programming for the solution of the optimization problems, and computer science in order to keep up-to-date to the capabilities that the computer technology offers today. This latter includes issues that range from hardware, to languages (FORTRAN, C++) and operating systems, to programming approaches (mathematical modeling vs experts systems and artificial intelligence) and software structure (modular, equation oriented, object oriented programming). The challenge in the development of computer aided design tools for the chemical industry, compared with the design of other manufacturing processes, is that the exact properties of the system are often only imperfectly known and there is neither the money nor the time available to study them sufficiently in order to acquire a more accurate knowledge (Rippin 1989). On the other hand, there are considerable benefits when such tools are employed regarding both the operability and profitability of the processes (Boston, 1990). Regarding the polymer manufacturing industry both the challenges and the rewards are distinctively amplified (Schnler and Schmidt 1992, Ray 1989). In contrast though to the general status and perspectives of computer aided design, the polymer engineer can find little help in the established software packages either because the pertinent modules are lacking completely or they are quite simplistic. The fact that polymer reactors stand as the typical example for what the computer aided design tools should "ultimately" address in the near future (Evans, 1994), increases the scope for the urgent development of CAD packages for the polymer industry. Furthermore, what is vaguely called a "polymer reactor" is a label for a whole field of diverse technologies. The diversity of the field can be better appreciated in Ray (1989) where a CAD software package, Poly(mer)Re(actor)D(esign), is presented. The software package provides a unified framework for modeling and simulation of polymerization processes. The software package handles various types of polymerization mechanisms (e.g. free-radical, ionic), various polymerization techniques (e.g. bulk, suspension, emulsion), and can assemble various reactor configurations (e.g. tubular, stirred tanks). But despite the package's high degree of "specialization", it is not an on-line optimization tool for it does not address the specific peculiarities of a polymer process (e.g. presence of varying amounts of
$449
$450
European Symposiumon Computer Aided Process Engineering--6. Part A
impurities, heat transfer fouling, etc.). As an example, a real stirred autoclave reactor would be the result of an assembly of standard CSTR, mixer and splitter units that are provided by the PolyReD framework. The task of the estimation of the parameters involved in such an assembly would be performed by applying a "driver" to the configured "flowsheet". This scheme however can hardly simulate the real reactor as the main problem in fitting this reactor, is exactly to identify the configuration of the ideal CSTR vessels (e.g. number of vessels and recycle streams) that best simulates the nonideal mixing conditions of the real stirred reactor. The fact that the area of polymer science and engineering is sewed by tenths of scientific periodicals and only a few reliable simulation software packages, is a consequence of the fact that the major software vendors are mainly occupied by the development of the software structure overhead, employing mainly the established unit modeling literature. This situation however proves to be incapable of coping with the immense and fast-evolving literature that deals with the polymer manufacturing processes. On the other hand, individual modeling experts in the diverse fields of polymer science and engineering are obviously lagging in the software structuring expertise and the state-of-the-art problem solving tools available to the major software ventors. A solution may be eventually found in the perspectives that Evans (1994) states in his review. According to Evans a flexible framework in an object oriented programming environment that could assemble data, models, solvers and design methods from different sources, should be established which will enable an organization to "develop" a customized CAD software package that will best suit its needs. The two software packages presented here follow a custom made approach and each handles a specific reactor type, that uses a single polymerization mechanism (free radical) for the production of a specific product (LOPE). In this sense they are a kind of specialty CAD tools for the polymer manufacturing industry. There is a necessity and a sufficiency factor involved in this specialization. The necessity factor is that the complexity and the peculiarities of the simulated technologies make it indispensable to embody in the simulator the specifics of the know-how developed around the operation of these units. The sufficiency factor, on the other hand, is that the extremely special software products developed handle two technologies that, taken together, produce almost ten per cent of all comodity polymers. HIGH PRESSURE TUBULAR AND AUTOCLAVES LOPE REACTORS A tubular LOPE reactor (Figure 1) consists of a spiral-wrapped metallic pipe with a large length to diameter ratio, with a total length ranging from 500 to 1500 m and an internal diameter not exceeding 60 m m The heat of reaction is partially removed through the reactor wall by a heat transfer fluid, resulting in a non-isothermal reactor operation. In relation to heat requirements of the process the reactor is divided into a number of zones (e.g. preheating, reaction and cooling zones). A commercial reactor can have multiple reaction and cooling zones, and includes a number of monomer, initiator, and chain-transfer agent side-feed points. The total conversion ranges between 20 and 35%, while the produced polymer has a density ranging from 0.915 to 0.93 g/cm3 and a melt flow index varying in the range of 0.1 to 150 g/10 mins.
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Figure 1: Schematic Diagram of a Three Zone Tubular LOPE Reactor
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Figure 2: Schematic Diagram of an Autoclave LOPE Reactor
A vessel reactor (Figure 2) is a constantly stirred autoclave that operates adiabatically under controlled temperature and pressure conditions. These vessels have a length-to-diameter ratio ranging from 4 to 1 up to 20 to 1 and are subdivided into a number of reaction zones Cmultizone" vessel). Reaction conditions may be adjusted separately in each zone to give polymers of a wide molecular range. The reactor can include a number of monomer, initiator, and chain-transfer agent side-feed points. Mixing is provided by a shaft running down the center of the reactor with impeller blades attached to it. Despite the very high power input per unit volume required
European Symposiumon Computer Aided Process Engineering--6. Part A
$451
tO maintain good mixing conditions, the flow behaviour deviates significantly from ideal flow patterns, leading to its approximation with complicated mixing models (Topalis et al., 1995) THE OVERALL STRUCTURE OF THE SOFTWARE PACKAGES The software packages presented here are powerfuU and flexible simulation and design tools. As such they can be used either to predict the molecular properties of the PE producexi or to simulate the control moves of key operating parameters (e.g. initiator and chain transfer agent flow rates), or even to predict the operational and product characteristics of alternative design options. The main functions of the packages have been built around a comprehensive mathematical model of the reactor (Kiparissides et al., 1993) that includes the energy and material balances, based on the leading moments of the Molecular Weight Distribution, and supported by the physical and molecular properties evaluation equations. The reaction mechanism includes: initiator decomposition, chain initiation and propagation, termination by combination and disproportionation, chain transfer to monomer, to solvent and polymer, intramolecular transfer and D-scission of sec and tert radicals. The basic reactor simulators are computationally stand alone programs. However, they are destined to fail without the services of the on-line tuning tools of the packages, which are the programs that appear as options ESTIMATOR-1 and ESTIMATOR-2 in Figure 3. It is exactly these tuning tools that promote the package from a typical desktop software to an on-line optimization tool, capable of providing model based predictive control services. There are two basic reasons that make these on-line tuning tools indispensable. The first is that due to the complexity of the physical and chemical phenomena occurring in the reactor, it is not possible to have from first principles an all predictive model of the reactor. The second is that the reactors change periodically their operating conditions in order to produce different PE grades. The level of sophistication and the assumptions made in the basic reactor model of the simulator are tuned by the trade-off between the accuracy and the speed requirements. More specifically, the model has been designed and built so as to be fast enough to be tuned on-line according to the time scales of the reactor's modes of operation and sophisticated enough so as to provide reliable predictions in between its periodical tunings. The function of the tuning tools is to fit the mathematical model of the reactor to the actual performance by adjusting a few key parameters that are internal to the model and could not anyhow be modeled from first principles (e.g. initiator efficiency or the non-ideal flow pattern in the autoclave). It is exactly these parameters that take off the slack between whatever is really happening in the reactors and what the model assumes it does. Critical decisions in the development of the programs were, on one hand, to determine the pivotal parameters that would effectively drive the model to simulate the real reactor and, on the other hand, to choose which reactor measurements represent accurately the status of the reactor; measurements that would have a functional impact on the model's adjustable parameters and in the same time would be sufficiently reliable. For this latter issue, the most appropriate choices proved to be the temperature measurements and the melt index of the product. The temperature measurements are used to capture the time varying state of the reactor and they have a dominant influence on the reactor's model performance. The melt index of the product can be measured down the reactor line and provides an overall estimate of the quality of the polymer. Actually, the working hypothesis that proved itself in practice, was that the mathematical model together with the temperature measurements along the reactor and a melt index measurement at the reactor end, could lead to an accurate representation of the actual reactors' performance. Regarding the model's parameters that are used to tune the model, the two technologies present several similarities but also some fundamental differences. The similarities have to do with the identical reaction mechanism that lies behind the two reactor models.The polymerization kinetics can be appropriately manipulated by interfering at two pivotal steps of the kinetic mechanism, namely, the initiation and chain transfer reactions that control the overall polymerization rate and molecular weight developments, respectively. The actual technology though that stands over the polymerization chemistry affects differently its performance. Thus, the common tuning parameters are the initiator and chain transfer agent efficiencies, whereas the tubular reactor has additionally the fouling factor, and the autoclaves the mixing parameters (as described in Topalis et al., 1995). The parameter estimation problem has been decomposed into different levels. The temperature profile fitting for the tubular reactor, is performed at each reactor zone using a local estimator which calculates the initiator efficiency and the fouling factor. For the autoclave reactor the pertinent estimator fits the temperature profile by evaluating the mixing model parameters (ESTIMATOR-1 in Figure 3). The melt index is then fitted by tuning the CTA efficiency along the reactor (ESTIMATOR-2 in Figure 3). Having tuned the model to the actual reactor performance, the model can reliably extrapolate and predict the reactors' behavior. This can be capitalized by inserting the estimated tuning parameters to the model and performing "what if' simulations by the simulator options, or evaluating control moves, or finally by optimizing the operating conditions of the reactor. The various functions of the software packages are pictured in Figure 3.
$452
European Symposiumon Computer Aided Process Engineering--6. Part A
M~N~UALA:ED
~
~1
-I
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Temperatures
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Temperatures ~ o r ~ .
1
.
.
.
.
.
Max Production
Figure 3 " Logical Diagram of the Various Functions of the Software Packages
STEPPING FROM AN ACADEMIC PROGRAM TO A SOFTWARE PRODUCT Having developed the programs outlined above, it has been considered essential to put them in a package that would facilitate the production engineer to use them and use them correctly and efficiently. Addressing this issue, several input/output facilities have been built around the programs that actually solve the problems. These facilities include: 1. Self explained, automatically created input and output-report files (see Tables 1 and 2) for each simulation, parameter estimation, control and optimization run. For this reason, a menu driven user friendly input/output interface has been built. According to this scheme, the user is asked only for the data needed (see Table 1) for the specific problem he/she wants to solve in an automatically handled hierarchy of input-data screens. 2. Although the software packages have focused on specific technologies, they have been built general enough to handle any possible realization of these technologies. This led to a considerable increase of the input data load. In order to facilitate the input phase, a number of databases have been attached to it, that load automatically whole sets of input data. Some databases created for the tubular reactor case are: a. Geometry Configuration: (i) The number and sequence (reacting-cooling) of reactor zones and (ii) The number of quenching and initiator injection points. b. Geometry of Each Zone: (i) The inside and outside diameter and (ii) The tube length and the number of tubes c. Initiator Properties: (I) Molecular Weight, (ii) Density, (iii) Frequency factor, Activation energy and activation volume of the decomposition reaction for each initiator. 3. A number of state and output variables (e.g. temperature profiles, number and weight average molecular weights, velocity, melt index, etc.) can be graphically represented. Figures 4 and 5 show typical temperature profiles of the tubular and the autoclave packages respectively. Recapitulating, the main thrust of the software packages developed, is the state-of-the-art model and physical properties estimation routines and the structuring of the parameter estimation problems, in accordance with the expertise of our laboratory. The facilities built around this expertise, were almost equally important in order to make it user-friendly and essentially proving it.
European Symposiumon ComputerAided ProcessEngineering--6. Part A
$453
Table 1 : Input Data Information -Tubular
Reactor Configuration
Number of Quenching Points, Initiator Inputs, Solvents & Modifiers, Coolant Systems Number of Reactor Zones, Tubes per Zone, Tube Length, Internal External Reactor Diameter, Jacket Diameter Temperature, Pressure, Flowrates, Composition, Position
Reactor Geometry Main Feed and Quenching Points Stream Information Initiator Injection Information Coolant Systems Information Input Parameters
Initiator Type, Flowrates and Injection Position Flowrates and Inlet / Outlet Temperatures for Each Reactor Zone Problem Specific(e.g. Initiator Efficiency and Fouling Factors, Solvent Efficiency) Problem Specific (e.B. Temperatures)
Reactor Data
Table 2 : Output Report Information -Tubular
Temperature Profile Information Reaction Mixture Information
Minimum/Maximum Temperature and Position Velocity,Pressure, Composition, Initiator Efficiency, Reynolds number and Overall Heat Transfer Coefficient. Flowrate, Inlet/Outlet Temperature and Heat Load Long and Short Chain Branching, Number and Weight Average Molecular Weights, Polydispersity Index, Density, Melt Index
Coolant System Details Product Detailed Information
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Figure 4 : Comparative Graph of the Temperature Figure 5 : The Steady-State Temperature Profile as Profile (Reactor Data vs Model Estimate) as Produced Produced by the Autoclave Reactor Package by the Tubular Reactor Package
$454
European Symposiumon ComputerAided ProcessEngineering---6.Part A
REFERENCES
Boston, J.F., 1990, Process Modelling Works, CHEMTECH, February, p. 100. Evans, L.B., 1994, Steady-State Simulation: A State-of-the-Art Review, Proceedings of PSE '94, pp. 953-967. Kiparissides, C., G. Verros, and J.F. MacGregor, 1993, Mathematical Modeling, Optimization, and Quality Control of High Pressure Ethylene Polymerization Reactors, J.M.S-Rev. Macromol. Chem. Phys., C33(4), pp. 437527. Ray, H.W., 1989, Computer-Aided Design, Monitoring and Control of Polymerization Processes, Polymer Reaction Engineering, Eds. K. H. Reichert and W.Geiseler, VCH Publishers, pp. 105-122. Rippin, D.W.T., 1990, Introduction:Approaches to Chemical Process Synthesis, Foundations of Computer-Aided Process Design, Eds. J.J. Siirola, I.E. Grossmann, and G. Stefanopoulos, pp. 75-79, Elsevier, Amsterdam. Schuler, H. and Schmidt, U, 1992, Automated Operation of Polymerization Reactors, 4th International Workshop on Polymer Reaction Engineering, K.H. Reichert, and H.U. Moritz, Eds., DECHEMA, 127, 235-255, VCH Publishers, Frankfurt. Topalis, E., P. Pladis, and C. Kiparissides, 1995, Computer Aided Design of Multizone, Multifeed, LDPE, Autoclaves, 5th International Workshop on Polymer Reaction Engineering, K.H. Reichert, and H.U. Moritz, Eds., DECHEMA, 131, 631-654, VCH Publishers, Frankfurt.
Pergamon
S0098-1354(96)00085-3
Copyright© 1996ElsevierScience Ltd Printed in Great Britain. All rights reserved 0098-1354/96$15.00+0.~X)
COMPUTER AIDED DESIGN OF POLYMER REACTORS
A. PERTSINIDIS, E. PAPADOPOULOS, and C. KIPARISSIDES
Chemical Engineering Department and Chemical Process Engineering Research Institute Aristotle University of Thessaloniki P.O. Box 472, Greece
Abstract - This paper describes the development of CAD packages for high pressure polyethylene reactors. The overall goal of the software packages was to develop powerfull, flexible, adaptive design and predictive simulation tools that can follow and predict the operating conditions of a given high pressure polymer reactor in an accurate, prompt and comprehensive way. Their range of use includes the prediction in real time of the molecular properties of polymer produced in high pressure LDPE reactors, the estimation of control moves of key process variables as well as the prediction of the operational and product characteristics of alternative design options. Major points of consideration during the program development were the user friendliness of the input/output and the execution speed enabling its online use as a predictive tool. The functions of the packages have been built around a modular mathematical model of the reactor that includes the energy and material balances based on the leading moments of the Molecular Weight Distribution equations and supported by the physical and molecular properties evaluation equations.
INTRODUCTION The development of computer-aided design (CAD) tools has advanced dramatically in the past fourty years. Furthermore, the perspectives for the near future developments are even more promising (Evans, 1994). The traditionally set path for the development of general purpose simulation packages requires a CAD expertise as an added value to the mathematical models of unit operations and physical properties evaluation, established in the literature. This modeling and problem solving expertise encompasses a wide variety of interdisciplinary fields such as numerical analysis for the solution of differential and algebraic equations, mathematical programming for the solution of the optimization problems, and computer science in order to keep up-to-date to the capabilities that the computer technology offers today. This latter includes issues that range from hardware, to languages (FORTRAN, C++) and operating systems, to programming approaches (mathematical modeling vs experts systems and artificial intelligence) and software structure (modular, equation oriented, object oriented programming). The challenge in the development of computer aided design tools for the chemical industry, compared with the design of other manufacturing processes, is that the exact properties of the system are often only imperfectly known and there is neither the money nor the time available to study them sufficiently in order to acquire a more accurate knowledge (Rippin 1989). On the other hand, there are considerable benefits when such tools are employed regarding both the operability and profitability of the processes (Boston, 1990). Regarding the polymer manufacturing industry both the challenges and the rewards are distinctively amplified (Schnler and Schmidt 1992, Ray 1989). In contrast though to the general status and perspectives of computer aided design, the polymer engineer can find little help in the established software packages either because the pertinent modules are lacking completely or they are quite simplistic. The fact that polymer reactors stand as the typical example for what the computer aided design tools should "ultimately" address in the near future (Evans, 1994), increases the scope for the urgent development of CAD packages for the polymer industry. Furthermore, what is vaguely called a "polymer reactor" is a label for a whole field of diverse technologies. The diversity of the field can be better appreciated in Ray (1989) where a CAD software package, Poly(mer)Re(actor)D(esign), is presented. The software package provides a unified framework for modeling and simulation of polymerization processes. The software package handles various types of polymerization mechanisms (e.g. free-radical, ionic), various polymerization techniques (e.g. bulk, suspension, emulsion), and can assemble various reactor configurations (e.g. tubular, stirred tanks). But despite the package's high degree of "specialization", it is not an on-line optimization tool for it does not address the specific peculiarities of a polymer process (e.g. presence of varying amounts of
$449
$450
European Symposiumon Computer Aided Process Engineering--6. Part A
impurities, heat transfer fouling, etc.). As an example, a real stirred autoclave reactor would be the result of an assembly of standard CSTR, mixer and splitter units that are provided by the PolyReD framework. The task of the estimation of the parameters involved in such an assembly would be performed by applying a "driver" to the configured "flowsheet". This scheme however can hardly simulate the real reactor as the main problem in fitting this reactor, is exactly to identify the configuration of the ideal CSTR vessels (e.g. number of vessels and recycle streams) that best simulates the nonideal mixing conditions of the real stirred reactor. The fact that the area of polymer science and engineering is sewed by tenths of scientific periodicals and only a few reliable simulation software packages, is a consequence of the fact that the major software vendors are mainly occupied by the development of the software structure overhead, employing mainly the established unit modeling literature. This situation however proves to be incapable of coping with the immense and fast-evolving literature that deals with the polymer manufacturing processes. On the other hand, individual modeling experts in the diverse fields of polymer science and engineering are obviously lagging in the software structuring expertise and the state-of-the-art problem solving tools available to the major software ventors. A solution may be eventually found in the perspectives that Evans (1994) states in his review. According to Evans a flexible framework in an object oriented programming environment that could assemble data, models, solvers and design methods from different sources, should be established which will enable an organization to "develop" a customized CAD software package that will best suit its needs. The two software packages presented here follow a custom made approach and each handles a specific reactor type, that uses a single polymerization mechanism (free radical) for the production of a specific product (LOPE). In this sense they are a kind of specialty CAD tools for the polymer manufacturing industry. There is a necessity and a sufficiency factor involved in this specialization. The necessity factor is that the complexity and the peculiarities of the simulated technologies make it indispensable to embody in the simulator the specifics of the know-how developed around the operation of these units. The sufficiency factor, on the other hand, is that the extremely special software products developed handle two technologies that, taken together, produce almost ten per cent of all comodity polymers. HIGH PRESSURE TUBULAR AND AUTOCLAVES LOPE REACTORS A tubular LOPE reactor (Figure 1) consists of a spiral-wrapped metallic pipe with a large length to diameter ratio, with a total length ranging from 500 to 1500 m and an internal diameter not exceeding 60 m m The heat of reaction is partially removed through the reactor wall by a heat transfer fluid, resulting in a non-isothermal reactor operation. In relation to heat requirements of the process the reactor is divided into a number of zones (e.g. preheating, reaction and cooling zones). A commercial reactor can have multiple reaction and cooling zones, and includes a number of monomer, initiator, and chain-transfer agent side-feed points. The total conversion ranges between 20 and 35%, while the produced polymer has a density ranging from 0.915 to 0.93 g/cm3 and a melt flow index varying in the range of 0.1 to 150 g/10 mins.
MO~OMZR
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...............
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Figure 1: Schematic Diagram of a Three Zone Tubular LOPE Reactor
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Figure 2: Schematic Diagram of an Autoclave LOPE Reactor
A vessel reactor (Figure 2) is a constantly stirred autoclave that operates adiabatically under controlled temperature and pressure conditions. These vessels have a length-to-diameter ratio ranging from 4 to 1 up to 20 to 1 and are subdivided into a number of reaction zones Cmultizone" vessel). Reaction conditions may be adjusted separately in each zone to give polymers of a wide molecular range. The reactor can include a number of monomer, initiator, and chain-transfer agent side-feed points. Mixing is provided by a shaft running down the center of the reactor with impeller blades attached to it. Despite the very high power input per unit volume required
European Symposiumon Computer Aided Process Engineering--6. Part A
$451
tO maintain good mixing conditions, the flow behaviour deviates significantly from ideal flow patterns, leading to its approximation with complicated mixing models (Topalis et al., 1995) THE OVERALL STRUCTURE OF THE SOFTWARE PACKAGES The software packages presented here are powerfuU and flexible simulation and design tools. As such they can be used either to predict the molecular properties of the PE producexi or to simulate the control moves of key operating parameters (e.g. initiator and chain transfer agent flow rates), or even to predict the operational and product characteristics of alternative design options. The main functions of the packages have been built around a comprehensive mathematical model of the reactor (Kiparissides et al., 1993) that includes the energy and material balances, based on the leading moments of the Molecular Weight Distribution, and supported by the physical and molecular properties evaluation equations. The reaction mechanism includes: initiator decomposition, chain initiation and propagation, termination by combination and disproportionation, chain transfer to monomer, to solvent and polymer, intramolecular transfer and D-scission of sec and tert radicals. The basic reactor simulators are computationally stand alone programs. However, they are destined to fail without the services of the on-line tuning tools of the packages, which are the programs that appear as options ESTIMATOR-1 and ESTIMATOR-2 in Figure 3. It is exactly these tuning tools that promote the package from a typical desktop software to an on-line optimization tool, capable of providing model based predictive control services. There are two basic reasons that make these on-line tuning tools indispensable. The first is that due to the complexity of the physical and chemical phenomena occurring in the reactor, it is not possible to have from first principles an all predictive model of the reactor. The second is that the reactors change periodically their operating conditions in order to produce different PE grades. The level of sophistication and the assumptions made in the basic reactor model of the simulator are tuned by the trade-off between the accuracy and the speed requirements. More specifically, the model has been designed and built so as to be fast enough to be tuned on-line according to the time scales of the reactor's modes of operation and sophisticated enough so as to provide reliable predictions in between its periodical tunings. The function of the tuning tools is to fit the mathematical model of the reactor to the actual performance by adjusting a few key parameters that are internal to the model and could not anyhow be modeled from first principles (e.g. initiator efficiency or the non-ideal flow pattern in the autoclave). It is exactly these parameters that take off the slack between whatever is really happening in the reactors and what the model assumes it does. Critical decisions in the development of the programs were, on one hand, to determine the pivotal parameters that would effectively drive the model to simulate the real reactor and, on the other hand, to choose which reactor measurements represent accurately the status of the reactor; measurements that would have a functional impact on the model's adjustable parameters and in the same time would be sufficiently reliable. For this latter issue, the most appropriate choices proved to be the temperature measurements and the melt index of the product. The temperature measurements are used to capture the time varying state of the reactor and they have a dominant influence on the reactor's model performance. The melt index of the product can be measured down the reactor line and provides an overall estimate of the quality of the polymer. Actually, the working hypothesis that proved itself in practice, was that the mathematical model together with the temperature measurements along the reactor and a melt index measurement at the reactor end, could lead to an accurate representation of the actual reactors' performance. Regarding the model's parameters that are used to tune the model, the two technologies present several similarities but also some fundamental differences. The similarities have to do with the identical reaction mechanism that lies behind the two reactor models.The polymerization kinetics can be appropriately manipulated by interfering at two pivotal steps of the kinetic mechanism, namely, the initiation and chain transfer reactions that control the overall polymerization rate and molecular weight developments, respectively. The actual technology though that stands over the polymerization chemistry affects differently its performance. Thus, the common tuning parameters are the initiator and chain transfer agent efficiencies, whereas the tubular reactor has additionally the fouling factor, and the autoclaves the mixing parameters (as described in Topalis et al., 1995). The parameter estimation problem has been decomposed into different levels. The temperature profile fitting for the tubular reactor, is performed at each reactor zone using a local estimator which calculates the initiator efficiency and the fouling factor. For the autoclave reactor the pertinent estimator fits the temperature profile by evaluating the mixing model parameters (ESTIMATOR-1 in Figure 3). The melt index is then fitted by tuning the CTA efficiency along the reactor (ESTIMATOR-2 in Figure 3). Having tuned the model to the actual reactor performance, the model can reliably extrapolate and predict the reactors' behavior. This can be capitalized by inserting the estimated tuning parameters to the model and performing "what if' simulations by the simulator options, or evaluating control moves, or finally by optimizing the operating conditions of the reactor. The various functions of the software packages are pictured in Figure 3.
$452
European Symposiumon Computer Aided Process Engineering--6. Part A
M~N~UALA:ED
~
~1
-I
REACTOR
Temperatures
TUBULAR Initiator Effiency Fouling
L
~...~INPUT ~
/ MANIPULATED VARIABLES
Melt Index
w SolventEfficiency
AUTOCLAVES Mixing model parameters i
I~ Simulation Results
CONTROL
",,e~em~
Melt I n d e x Product Density
i
OPTIMUMnlll
Temperatures ~ o r ~ .
1
.
.
.
.
.
Max Production
Figure 3 " Logical Diagram of the Various Functions of the Software Packages
STEPPING FROM AN ACADEMIC PROGRAM TO A SOFTWARE PRODUCT Having developed the programs outlined above, it has been considered essential to put them in a package that would facilitate the production engineer to use them and use them correctly and efficiently. Addressing this issue, several input/output facilities have been built around the programs that actually solve the problems. These facilities include: 1. Self explained, automatically created input and output-report files (see Tables 1 and 2) for each simulation, parameter estimation, control and optimization run. For this reason, a menu driven user friendly input/output interface has been built. According to this scheme, the user is asked only for the data needed (see Table 1) for the specific problem he/she wants to solve in an automatically handled hierarchy of input-data screens. 2. Although the software packages have focused on specific technologies, they have been built general enough to handle any possible realization of these technologies. This led to a considerable increase of the input data load. In order to facilitate the input phase, a number of databases have been attached to it, that load automatically whole sets of input data. Some databases created for the tubular reactor case are: a. Geometry Configuration: (i) The number and sequence (reacting-cooling) of reactor zones and (ii) The number of quenching and initiator injection points. b. Geometry of Each Zone: (i) The inside and outside diameter and (ii) The tube length and the number of tubes c. Initiator Properties: (I) Molecular Weight, (ii) Density, (iii) Frequency factor, Activation energy and activation volume of the decomposition reaction for each initiator. 3. A number of state and output variables (e.g. temperature profiles, number and weight average molecular weights, velocity, melt index, etc.) can be graphically represented. Figures 4 and 5 show typical temperature profiles of the tubular and the autoclave packages respectively. Recapitulating, the main thrust of the software packages developed, is the state-of-the-art model and physical properties estimation routines and the structuring of the parameter estimation problems, in accordance with the expertise of our laboratory. The facilities built around this expertise, were almost equally important in order to make it user-friendly and essentially proving it.
European Symposiumon ComputerAided ProcessEngineering--6. Part A
$453
Table 1 : Input Data Information -Tubular
Reactor Configuration
Number of Quenching Points, Initiator Inputs, Solvents & Modifiers, Coolant Systems Number of Reactor Zones, Tubes per Zone, Tube Length, Internal External Reactor Diameter, Jacket Diameter Temperature, Pressure, Flowrates, Composition, Position
Reactor Geometry Main Feed and Quenching Points Stream Information Initiator Injection Information Coolant Systems Information Input Parameters
Initiator Type, Flowrates and Injection Position Flowrates and Inlet / Outlet Temperatures for Each Reactor Zone Problem Specific(e.g. Initiator Efficiency and Fouling Factors, Solvent Efficiency) Problem Specific (e.B. Temperatures)
Reactor Data
Table 2 : Output Report Information -Tubular
Temperature Profile Information Reaction Mixture Information
Minimum/Maximum Temperature and Position Velocity,Pressure, Composition, Initiator Efficiency, Reynolds number and Overall Heat Transfer Coefficient. Flowrate, Inlet/Outlet Temperature and Heat Load Long and Short Chain Branching, Number and Weight Average Molecular Weights, Polydispersity Index, Density, Melt Index
Coolant System Details Product Detailed Information
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$454
European Symposiumon ComputerAided ProcessEngineering---6.Part A
REFERENCES
Boston, J.F., 1990, Process Modelling Works, CHEMTECH, February, p. 100. Evans, L.B., 1994, Steady-State Simulation: A State-of-the-Art Review, Proceedings of PSE '94, pp. 953-967. Kiparissides, C., G. Verros, and J.F. MacGregor, 1993, Mathematical Modeling, Optimization, and Quality Control of High Pressure Ethylene Polymerization Reactors, J.M.S-Rev. Macromol. Chem. Phys., C33(4), pp. 437527. Ray, H.W., 1989, Computer-Aided Design, Monitoring and Control of Polymerization Processes, Polymer Reaction Engineering, Eds. K. H. Reichert and W.Geiseler, VCH Publishers, pp. 105-122. Rippin, D.W.T., 1990, Introduction:Approaches to Chemical Process Synthesis, Foundations of Computer-Aided Process Design, Eds. J.J. Siirola, I.E. Grossmann, and G. Stefanopoulos, pp. 75-79, Elsevier, Amsterdam. Schuler, H. and Schmidt, U, 1992, Automated Operation of Polymerization Reactors, 4th International Workshop on Polymer Reaction Engineering, K.H. Reichert, and H.U. Moritz, Eds., DECHEMA, 127, 235-255, VCH Publishers, Frankfurt. Topalis, E., P. Pladis, and C. Kiparissides, 1995, Computer Aided Design of Multizone, Multifeed, LDPE, Autoclaves, 5th International Workshop on Polymer Reaction Engineering, K.H. Reichert, and H.U. Moritz, Eds., DECHEMA, 131, 631-654, VCH Publishers, Frankfurt.