Design, Simulation and Optimization of Polymerization Processes Using Advanced Open Architecture Software Tools

20th European Symposium on Computer Aided Process Engineering – ESCAPE20 S. Pierucci and G. Buzzi Ferraris (Editors) © 2010 Elsevier B.V. All rights reserved.



Design, Simulation and Optimization of Polymerization Processes Using Advanced Open Architecture Software Tools Apostolos Krallisa, Prokopis Pladisb, Vassilis Kanellopoulosb, Vassilis Saliakasb, Vassilis Touloupides c and Costas Kiparissidesa,b,c D

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Abstract As the polymer industry becomes more global and competitive pressures are intensifying, polymer manufacturers recognize the need for the development of advanced process simulators for polymer plants. The overall goal is to utilize powerful, flexible, adaptive design and predictive simulation tools that can follow and predict the behaviour of polymer production processes in an accurate, prompt and comprehensive way. In response to the current needs, a new generation of software packages has been developed for the simulation, design, parameter and state estimation, optimization and control of specific polymerization processes aiming at increasing plant efficiency, improving product quality and reducing the impact to environment. The new software tools provide a user-friendly interface, including an object-oriented design environment that can be accessed from the engineer’s windows-based desktop environment and provide full graphical interaction and expert system guidance on how to use the program or making engineering decisions (such as selection of unit operation or physical property method). Moreover, an open-system architecture is adopted and applied to the process modeling components (PMCs) in order to be transparent to any other compatible process modeling environment (PME). Finally, recent advances regarding the development of software applications for specific polymerization systems (i.e., an LDPE high-pressure tubular reactor process and a PVC batch suspension process) are presented. Keywords: Computer Aided Design, Polymerization Processes, Software Packages, Object Oriented Design

1. Introduction Product quality, plant efficiency and safety can be significantly improved by the use of process models. A mathematical model that can reliably predict the behavior of a specific unit or/and process becomes a valuable tool that can be applied to all tasks of process operation. Software modularity, user friendly interfaces and computing power have increasingly opened up new opportunities for the application of advanced mathematical models in process operations. This growing computational potential has made possible the use of the same mathematical model for solving different problems of process operation hierarchy across the plant’s life-cycle. Indeed, the broader use of





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these models in process design, simulation, optimization and control, promises to have a profound commercial impact on the chemical and biochemical industry. Regarding the polymer manufacturing industry both the challenges and the rewards are distinctively amplified via the application of advanced mathematical models and computer-aided process simulators. However, in contrast to the advances in CAD of chemical processes, the polymer engineer cannot always find help in the established general-purpose software packages either because the pertinent process modules are lacking completely or they are quite simplistic. The fact that each polymerization process involves a number of unique physical and chemical phenomena (e.g., reaction kinetics, physical and transport phenomena, thermodynamics, etc.) increases the scope for the development of custom-made CAD software tools for specific polymerization processes. Thus, the adoption of an open-system architecture should be a major feature of a custom-made simulator for a specific polymerization process so that the user can select or/and combine available in-house software or/and specific routines obtained from different software vendors in order to build his own process simulator. In the present work, recent advances regarding the development of CAD software tools for two specific polymerization systems (i.e., an LDPE high-pressure tubular reactor process and a PVC batch suspension process), are presented. Our new PMCs can be used for the simulation, design, parameter and state estimation, optimization and control of polymerization processes. An object-oriented approach has been utilized so that the developed PMCs (i.e., polymerization reactor models) can be connected with other upstream or downstream equipment models (e.g., separators, heat exchangers, etc.) obtained from other sources (e.g., ASPEN). The adopted open-system architecture for PMCs largely facilitates the development of reusable components and offers additional advantages for standardization in process modeling technology.

2. Software Functionality and User Interface In the present work, a series of advanced software packages have been developed to simulate the dynamic operation of a great number of polymerization systems. These tools provide an easy-to-use environment with a variety of interfaces including a fully interactive process flow diagram (PFD) (see Figure 1). Reaction materials, polymerization kinetics, different reactor configurations and other unit operations can be readily selected from the simulator’s menu and combined together to build a desired process flow-sheet. Full access to kinetic parameters, species properties and concentrations, as well as process conditions allows the faithful description and simulation of a polymerization process. The adopted object-oriented design and open-system architecture make it possible to simulate polymerization processes in a simple and straightforward way. Such easyto-use validated process models can bring significant economic benefits to a polymer producer, including throughput and yield optimization, process trouble-shooting and analysis, grade transition strategies, development of new grades, process debottlenecking and design of advanced control systems. The developed software tools support the application of model-based approaches to the design, operation, optimization and control of polymerization processes. Consequently, the model of a specific polymerization process becomes the central repository of much of the available process knowledge. This may encompass both detailed understanding of fundamental physical phenomena and empirical knowledge gained from practice that, in general, leads to a ‘hybrid mechanistic model’. Ideally, such a model can predict the steady-state and dynamic behavior of a process over a

Design, Simulation and Optimization of Polymerization Processes Using Advanced  Open Architecture Software Tools



wide range of operating conditions to an acceptable degree of accuracy. The developed polymerization models (marketed by PolymerS Ltd) are easy to use and have been extensively validated against laboratory as well as pilot- and industrial-scale plant measurements. An ‘open-system architecture’ has been adopted that allows the export and, thus, integration of the developed PMCs with other compliant PMEs. In Figure 1, a schematic representation of the conceptual implementation of compatible PMCs of PolymerS Ltd, simulating a high-pressure LDPE tubular reactor and the operation of high- and lowpressure flash separators, within a commercial process simulator (e.g., ASPEN) is depicted. Notice that models for the compressors can be provided by the host PME, while the operation of the extruder can be simulated by a co-compliant PMC obtained by a third party. The heart of a process simulator is its user-interface environment that provides the means for simulation of a polymerization process, ranging from a single unit to an entire plant. This can be achieved by combining various PMCs from a database of available unit operation models. For this purpose, a standard flow-sheeting user interface has been developed comprising a palette of icons, each representing a different unit operation model in the library database, and a ‘white space area’ used for constructing and editing a new process flow-sheet (see Figure 1). Thus, the user can create an ‘instance’ of a process unit model by dragging an icon from the palette and dropping it in the white space. A ‘unit’ placed on the white space can be configured by the user via dialog windows that allow the specification of materials, unit design parameters (e.g., reactor and jacket geometric characteristics, controller parameters, etc.), operating conditions (e.g., temperature, pressure, materials flow rates, etc.), kinetic, transport and thermodynamic properties, etc. Each unit has one or more ports that allow its ‘instance’ to be connected to instances of other models in the flow-sheet. Standard graphical means are provided for effecting such connections. Once the process flow-sheet is built, then various simulation, optimization and parameter estimation runs may be initiated from it.

Exported PMCs

Figure 1: Screenshot of PolymerS software package.





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3. Polymerization Process Models A process simulation study involves a sequence of stages starting with the definition of the goals and is usually carried out in an iterative manner (Carido, 2009). In particular, the simulation study includes the conceptual phase (i.e., definition of the problem, possible collection of process data and definition of the conceptual model), the development phase (i.e., development of the model, simulation and model verification) and the post development actions (i.e., process optimization and control). The model implementation is usually carried out using a high-level programming language or an integrated software simulation environment. Process modeling and simulation, process optimization and control can have a significant impact on polymer plant’s operability and economics. Polymer manufacturers face increasing pressures for production cost reductions and more stringent quality requirements. However, product quality in polymer manufacturing is a much more complex issue than in more conventional simple chemical systems. Thus, a major objective of polymerization process modeling and simulation is to understand how the reaction mechanism, the physical transport properties (e.g., mass and heat transfer), reactor type and operating conditions affect the ‘polymer quality’ (Kiparissides, 2006). The last term includes all the polymer molecular properties (e.g., molecular weight distribution (MWD), copolymer composition distribution (CCD), long-chain branching (LCB), bivariate molecular weight – long chain branching distribution (MW-LCBD), topological properties of polymer chains, etc.) as well as the morphological properties of the product (i.e., particle size distribution (PSD), pore size distribution, bulk density, etc.). Since the end-use properties (i.e., physical, chemical, mechanical, rheological, etc.) of the polymers are directly linked with the molecular characteristics of the polymer chains, control of the polymer chain microstructure is of profound interest to the polymer manufacturing industry. This presupposes a thorough knowledge of the polymerization kinetics and the availability of advanced mathematical models to quantify the effects of process operating conditions on the molecular and morphological properties. Over the past thirty years, a great number of mathematical models to simulate the dynamic or steady-state operation of various polymerization processes have been developed in our laboratory. Our process-specific software packages include comprehensive kinetic, thermodynamic and mass-transfer models coupled with macroscopic energy and mass balances as well as with population balance models that are numerically integrated to provide unique information on the ‘polymer quality’, polymer productivity, morphological polymer properties, polymer melt rheological behavior, etc. (Meimaroglou et. al., 2007; Kiparissides et. al., 2005, Krallis et. al., 2004). Linear and non-linear programming algorithms are used for the off-line and online parameter and state estimation in the various polymerization models. Finally, optimization schemes are implemented to determine the optimal control policies to improve polymer quality, maximize reactor throughput, minimize energy consumption or/and the amount of off-spec polymer during a grade transition.

4. Polymer Process Simulators /'3(7XE6LPXODWRUŒ: The LDPE Tub Simulator™ is a powerful software tool developed for the simulation, parameter estimation, optimization and control of industrial high-pressure LDPE tubular reactor processes. It can be used either to predict the molecular properties of the ethylene homopolymer or copolymer grades (i.e., LDPE, EVA, EMA, EEA, EBA, EAA, etc.), to simulate the control moves of key operating

Design, Simulation and Optimization of Polymerization Processes Using Advanced  Open Architecture Software Tools parameters (e.g. initiator and chain transfer agent flow rates), or even to predict the operational and product characteristics of alternative design options. The overall goal of the software package is to provide adaptive design and predictive simulation tools that can follow and predict the operating conditions of a given high pressure polymer reactor process in an accurate, prompt and comprehensive way. Its range of use includes the prediction of molecular properties of the polymer produced, the estimation 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 are the user friendliness of the input/output and the execution speed enabling its online use as a predictive tool (Kiparissides et. al., 2005). The design of the reactor module is flexible enough to allow the incorporation of alternative reactor configurations, multiple injection points for monomers, initiators and solvents as well as multiple coolant streams. The simulator’s output include reactor and coolant temperature profiles, reactor pressure, monomer conversion, polymer molecular properties (e.g. number, weight and Z-average molecular weights, polydispersity, long chain branching, short chain branching, vinyl and vinylidene groups, polymer density, melt index, copolymer composition), heat transfer variables (e.g. overall, inside and coolant heat transfer coefficients, fouling coefficient), transport properties (solution viscosity, mixture velocity, Reynolds number) etc. The simulation results can be compared with available experimental measurements of the LDPE plant. Figure 2 depicts a comparison between experimental measurements and simulation results on polymerization temperature. The red line represents the variation of monomer conversion with respect to the tubular reactor length. The LDPE Tub Simulator™ incorporates as well software modules for the dynamic simulation of high-and low-pressure separation units, compressors, heat exchangers, etc., creating a process simulation tool. The process simulation studies can be used to optimize a grade or to perform grade transitions policies to minimize the undesired (offspec) polymer product. Finally, the optimization modules are employed to find the optimum operating conditions (e.g. initiator flowrates, coolant flowrates) that maximize the reactor productivity at the desired polymer quality. Moreover, the on-line implementation modules provide the necessary tools for the on-line real-time use of the simulator package. 39& 6LPXODWRUŒ: Various kinetic, thermodynamic and mass-transfer models coupled with macroscopic energy and mass balances as well as with population balance models are numerically integrated to provide unique information concerning the polymer productivity, molecular and morphological polymer properties, energy process requirements, etc. A multi-phase kinetic model is employed to predict monomer conversion, polymerization rate, reaction heat, time evolution of the reactor pressure, thermodynamic properties of the reaction mixture as well as the monomer distribution in the monomer-rich, polymer-rich, gas and aqueous phases.  The model also predicts the dynamic evolution of polymer molecular properties (e.g., number and weight average molecular weights, number of short chain branches, etc). Based on macroscopic mass and energy balances, the simulator provides important information on the species concentrations and polymer temperature profiles in the reactor, the jacket and the overhead condenser as well. A detailed dynamic population balance model is used to predict the time evolution of the transient droplet size / particle size distribution. A dynamic population balance model is utilized to predict the evolution of the internal particle morphology (e.g., primary particle size distribution, average porosity, etc.) (see Figure 3). Moreover, dynamic optimization modules are





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utilized in order to provide the optimal quantities of initiators, initiator addition strategies or reactor temperature profiles that produce a square polymerization rate profile capable of minimizing batch time.

Figure 2: Comparison of model predictions with experimental data in a high-pressure LDPE tubular reactor (LDPE Tub Simulator™)

Figure 3: Prediction of PVC Internal Particle Morphology of PVC Polymer Particles using the PVC Simulator®

5. Conclusions The present paper describes the development of an open-system platform for the computer aided design, simulation, parameter estimation and optimization of industrial polymer production processes. The utilization of an object-oriented programming environment is a major feature providing the user with the capability of selecting equipment relevant to the specific process, connecting the unit operations with other upstream and downstream equipment and, thus, building the process flow-sheet in an easy and comprehensive way. State-of-the-art mathematical models (e.g., kinetic models, thermodynamic models, mass-transfer models, population balance models, enduse properties models, etc.) capturing the peculiarities of specific polymerization processes can provide accurate information regarding polymer productivity, molecular, morphological, end-use polymer properties, etc. Finally, on-line and off-line optimization algorithms can be utilized to calculate the optimal operating conditions that can drive a polymerization process to some desired final properties specifications and, thus, gaining significant benefits on the polymer plant operability and economics.

References J.M. Carrido (2009). Object Oriented Simulation. A Modeling and Programming Perspective, Springer Science & Business Media, New York, USA. C. Kiparissides (2006). Chalenges in Polymerization Reactor Modeling and Optimization: A Population Balance Perspective, -RXUQDORI3URFHVV&RQWURO, 16, 205-224. D. Meimaroglou, A. Krallis, V. Saliakas, C. Kiparissides (2007). Prediction of the Bivariate Molecular Weight – Long Chain Branching Distribution in Highly Branched Polymerization Systems Using Monte Carlo and Sectional Grid Methods,0DFURPROHFXOHV, 40, 2224-2234. C. Kiparissides, C. Baltsas, E. Papadopoulos, J.P. Congalidis, J.R. Richards, M.B. Kelly, Ye Yi (2005). Mathematical Modeling of Free-Radical Ethylene Copolymerization in High-Pressure Tubular Reactors, Industrial Engineering and Chemistry Research, 44, 2592-2609. A. Krallis, C. Kotoulas, S. Papadopoulos, C. Kiparissides, J. Bousquet, C. Bonardi (2004). A Comprehensive Kinetric Model for the Free-Radical Polymerization of Vinyl Chloride in the Presence of Monofunctional and Bifunctional Initiators, ,QGXVWULDO(QJLQHHULQJDQG&KHPLVWU\ 5HVHDUFK 43, 6382-6399.