Modeling and Simulation of Polymerization Processes

This reprint is a compilation of nine papers published in Processes, in a Special Issue on “Modeling and Simulation of Polymerization Processes”. It aimed to address both new findings on basic topics and the modeling of the emerging aspects of product design and polymerization processes. It provides a nice view of the state of the art with regard to the modeling and simulation of polymerization processes. The use of well-established methods (e.g., the method of moments) and relatively more recent modeling approaches (e.g., Monte Carlo stochastic modeling) to describe polymerization processes of long-standing interest in industry (e.g., rubber emulsion polymerization) to polymerization systems of more modern interest (e.g., RDRP and plastic pyrolysis processes) are comprehensively covered in the papers contained in this reprint

A Model-Based Framework for the Smart Manufacturing of Polymers

It is hard to point a daily activity in which polymeric materials or plastics are not involved. The synthesis of polymers occurs by reacting small molecules together to form, under certain conditions, long molecules. In polymer synthesis, it is mandatory to assure uniformity between batches, high-quality of end-products, efficiency, minimum environmental impact, and safety. It remains as a major challenge the establishment of operational conditions capable of achieving all objectives together. In this dissertation, different model-centric strategies are combined, assessed, and tested for two polymerization systems. The first system is the synthesis of polyacrylamide in aqueous solution using potassium persulfate as initiator in a semi-batch reactor. In this system, the proposed framework integrates nonlinear modelling, dynamic optimization, advanced control, and nonlinear state estimation. The objectives include the achievement of desired polymer characteristics through feedback control and a complete motoring during the reaction. The estimated properties are close to experimental values, and there is a visible noise reduction. A 42% improvement of set point accomplishment in average is observed when comparing feedback control combined with a hybrid discrete-time extended Kalman filter (h-DEKF) and feedback control only. The 4-state geometric observer (GO) with passive structure, another state estimation strategy, shows the best performance. Besides achieving smooth signal processing, the observer improves 52% the estimation of the final molecular weight distribution when compared with the h-DEKF. The second system corresponds to the copolymerization of ethylene with 1,9-decadiene using a metallocene catalyst in a semi-batch reactor. The evaluated operating conditions consider different diene concentrations and reaction temperatures. Initially, the nonlinear model is validated followed by a global sensitivity analysis, which permits the selection of the important parameters. Afterwards, the most important kinetic parameters are estimated online using an extended Kalman filter (EKF), a variation of the GO that uses a preconditioner, and a data-driven strategy referred as the retrospective cost model refinement (RCMR) algorithm. The first two strategies improve the measured signal, but fail to predict other properties. The RCMR algorithm demonstrates an adequate estimation of the unknown parameters, and the estimates converge close to theoretical values without requiring prior knowledge

Optimal control of particle size distribution in semi-batch emulsion polymerisation

Imperial Users onl

Towards a framework for the development of control-oriented multiscale models of dynamical systems: semibatch emulsion polymerization case study

ABSTRACT: This work develops a framework for the construction of a control-oriented model from a multiscale perspective, using a semibatch emulsion polymerization process as a case study. First, a so-called full multiscale model (considering the macro-, meso-, and micro-scopic scales) was developed which is composed of a set of Partial/Ordinary Differential Equations and a kinetic Monte Carlo simulation (PDE/ODE - kMC). Then, to obtain a reduced-order representation of the multiscale model, Variance Algebra concepts are used as a tool for representing, at the mesoscopic scale, a disperse-phase system from which only statistical information is available. After that, a dataset considering several process operational conditions is built to capture the main dynamics at the microscopic scale. This dataset is used to derive a closed-form model of the microscopic state variables by adopting a statistical modeling approach. The final obtained control-oriented model is composed of a set of ODEs comprising the macroscopic and the mesoscopic scales that can be solved by using standard ODEs integration schemes, whereas the microscopic scale variables are conveniently defined as some of the system outputs, represented by a set of algebraic equations. In order to consistently solve the full multiscale model, a numerical scheme based on the Finite Element Method is developed capturing the nonlinear evolution of the Particle Size Distribution (PSD). The validity of the obtained reduced-order model is verified through several simulations with respect to the system inputs. Finally, the multiscale control-oriented representation is employed to perform a batch output-controllability analysis based on a set-theoretic approach. The proposed framework might be adopted as a tool for the derivation of dynamical multiscale models keeping a good balance between their tractability and predictive capability, which can constitute an advantage when implementing real-time optimization and process control

Book of abstracts of the 10th International Chemical and Biological Engineering Conference: CHEMPOR 2008

This book contains the extended abstracts presented at the 10th International Chemical and Biological Engineering Conference - CHEMPOR 2008, held in Braga, Portugal, over 3 days, from the 4th to the 6th of September, 2008. Previous editions took place in Lisboa (1975, 1889, 1998), Braga (1978), Póvoa de Varzim (1981), Coimbra (1985, 2005), Porto (1993), and Aveiro (2001). The conference was jointly organized by the University of Minho, “Ordem dos Engenheiros”, and the IBB - Institute for Biotechnology and Bioengineering with the usual support of the “Sociedade Portuguesa de Química” and, by the first time, of the “Sociedade Portuguesa de Biotecnologia”. Thirty years elapsed since CHEMPOR was held at the University of Minho, organized by T.R. Bott, D. Allen, A. Bridgwater, J.J.B. Romero, L.J.S. Soares and J.D.R.S. Pinheiro. We are fortunate to have Profs. Bott, Soares and Pinheiro in the Honor Committee of this 10th edition, under the high Patronage of his Excellency the President of the Portuguese Republic, Prof. Aníbal Cavaco Silva. The opening ceremony will confer Prof. Bott with a “Long Term Achievement” award acknowledging the important contribution Prof. Bott brought along more than 30 years to the development of the Chemical Engineering science, to the launch of CHEMPOR series and specially to the University of Minho. Prof. Bott’s inaugural lecture will address the importance of effective energy management in processing operations, particularly in the effectiveness of heat recovery and the associated reduction in greenhouse gas emission from combustion processes. The CHEMPOR series traditionally brings together both young and established researchers and end users to discuss recent developments in different areas of Chemical Engineering. The scope of this edition is broadening out by including the Biological Engineering research. One of the major core areas of the conference program is life quality, due to the importance that Chemical and Biological Engineering plays in this area. “Integration of Life Sciences & Engineering” and “Sustainable Process-Product Development through Green Chemistry” are two of the leading themes with papers addressing such important issues. This is complemented with additional leading themes including “Advancing the Chemical and Biological Engineering Fundamentals”, “Multi-Scale and/or Multi-Disciplinary Approach to Process-Product Innovation”, “Systematic Methods and Tools for Managing the Complexity”, and “Educating Chemical and Biological Engineers for Coming Challenges” which define the extended abstracts arrangements along this book. A total of 516 extended abstracts are included in the book, consisting of 7 invited lecturers, 15 keynote, 105 short oral presentations given in 5 parallel sessions, along with 6 slots for viewing 389 poster presentations. Full papers are jointly included in the companion Proceedings in CD-ROM. All papers have been reviewed and we are grateful to the members of scientific and organizing committees for their evaluations. It was an intensive task since 610 submitted abstracts from 45 countries were received. It has been an honor for us to contribute to setting up CHEMPOR 2008 during almost two years. We wish to thank the authors who have contributed to yield a high scientific standard to the program. We are thankful to the sponsors who have contributed decisively to this event. We also extend our gratefulness to all those who, through their dedicated efforts, have assisted us in this task. On behalf of the Scientific and Organizing Committees we wish you that together with an interesting reading, the scientific program and the social moments organized will be memorable for all.Fundação para a Ciência e a Tecnologia (FCT

Tracing back the source of contamination

From the time a contaminant is detected in an observation well, the question of where and when the contaminant was introduced in the aquifer needs an answer. Many techniques have been proposed to answer this question, but virtually all of them assume that the aquifer and its dynamics are perfectly known. This work discusses a new approach for the simultaneous identification of the contaminant source location and the spatial variability of hydraulic conductivity in an aquifer which has been validated on synthetic and laboratory experiments and which is in the process of being validated on a real aquifer

Population balance model-based optimal control of batch crystallisation processes for systematic crystal size distribution design

During recent years crystallisation has found applications in many chemical industries, such as pharmaceutical, petrochemical, micro-electronics and food industries. Crystallisation is a basic step for purification or separation for a large variety of organic, inorganic and pharmaceutical compounds. Most of the product qualities are directly related to the shape of the crystal size distribution (CSD). The main difficulty in batch crystallisation processes is to accomplish a uniform and reproducible CSD. On-line control during the process allows for improved crystalline product quality, shorter process times and reduction or elimination of compromised batches. The actual prediction and estimation of the shape of the distribution at the end of the batch can provide useful information for monitoring or designing the operating curve for the supersaturation controller. Model-based approaches provide consistency of the CSD, can be used for better control and also for product design by reverse engineering the process to achieve the desired CSD and shape. This research presents a novel methodology for solving the population balance equation (PBE) for the estimation of the shape of the crystal size distribution for batch crystallisation processes. The approach combines the quadrature method of moments (QMOM) and the method of characteristics (MOCH), and provides a computationally efficient technique for the reconstruction of the whole crystal size distribution. The technique was used to estimate the kinetic parameters for the size-dependent growth and secondary nucleation, for potash alum-water system using industrial pilot plant data provided by BASF, Chemical Company. The combined technique was also used to estimate the size-dependent dissolution parameters for potash alum-water system, using laboratory scale data. The QMOM-MOCH solution approach is evaluated in a model-based dynamic optimization study, with the aim to obtain the optimal temperature profiles, which drive the system in both the supersaturated and under-saturated region, to achieve desired target CSD. Using growth, dissolution and nucleation parameters the technique was used to optimise the temperature trajectories to obtain bimodal and mono-modal distributions. The technique can serve as a soft sensor for predicting the CSD, or as a computationally efficient algorithm for off-line design or on-line adaptation of operating policies based on knowledge of the full CSD data. Additionally, the PBE model was solved using the method of characteristics under the assumption of constant supersaturation. At constant supersaturation growth is the dominating phenomenon, yielding a simplified analytical expression for the prediction of the CSD. The research presents the new methodology for the systematic design of the setpoint operating curves for supersaturation controlled crystallisation processes, which produces a desired target crystal size distribution (CSD) at the end of the batch. A design parameter, was introduced as a function of the supersaturation and time, and is evaluated for supersaturation controlled processes. Based on the design parameter and the simplified analytical model, the supersaturation setpoint and batch time are determined using an optimisation approach to obtain a target distribution with a desired shape. Two additional methods are also proposed that use the seed in conjunction with the supersaturation setpoint design, and analytical CSD estimator for shaping the product CSD. The first approach designs a seed recipe as a mixture of crystals resulting for example from standard sieve analysis. In this approach the seed was introduced at the beginning of the batch. The second approach introduces the dynamic seeding concept, which allows an easily implementable methodology to achieve complex target CSDs using seed with mono-modal distribution as a process actuator. These methodologies were validated for potassium dichromate-water system. Size-dependent growth kinetic parameters for the potassium dichromate-water system were identified using as experimental setup developed at Loughborough University. The experiments presented in the thesis also illustrates the simultaneous application of in situ Process Analytical Technology (PAT) tools, such as focused beam reflectance measurement (FBRM) for nucleation detection, attenuated total reflection (ATR) UV/Vis spectroscopy for concentration monitoring, as well as the in-line use of a Mastersizer for real-time CSD measurement in the case of the potassium dichromate in water system. The approaches provide a comprehensive framework for model-based dynamic optimisation of crystallisation processes, which combines efficient numerical solution approaches of the PBE with the formulation of novel optimisation problems. The techniques presented include controlled dissolution, simultaneous optimisation of operating policies and seed recipes and dynamic seeding. Simulation and experimental evaluations of the proposed approaches demonstrate the potential of the techniques to provide significant improvement in the current state-of-the-art in crystallisation control

Multi-objective optimization and model-based predictive control using state feedback linearization for crystallization

The ongoing Quality-by-Design paradigm shift in the pharmaceutical industry has sparked a new interest in exploring advanced process control techniques to aid the efficient manufacture of high value products. One important process in the manufacturing is crystallization, a key process in purification of active pharmaceutical ingredients (APIs). There has been little crystallization control research in the area of global input/output linearization, otherwise referred to as state-feedback linearization (SFL). The global linearization allows a nonlinear model to be linearized over the whole domain for which the model is valid and can be embedded into a model predictive controller (MPC). MPC allows the control of a process based on a model which captures the physical understanding and constraints, but a widely reported challenge with the SFL technique is the poor ability of explicitly handling the plant constraints, which is not ideal for a highly regulated production environment such as pharmaceutical manufacturing. Therefore, the first purpose of this research is to explore the use of SFL and how it can be applied to controlling batch and continuous MSMPR crystallization processes with the incorporation of plant constraints in the MPC (named SFL-Plant constraints). The contribution made from this research is the exploration of the SFL MPC technique with successful implementation of SFL-Plant constraints. The novelty in this method is that the technique builds on existing SFL-MPC frameworks to incorporate a nonlinear constraints routine which handles plant constraints. The technique is applied on numerous scenarios of batch and continuous mixed suspension mixed product removal (MSMPR) supersaturation control of paracetamol in water, both seeded and unseeded, which all show that the SFL-Plant constraints technique indeed produces feasible control over crystallization subject to constraints imposed by limitations such as heat transfer. The SFL-MPC with SFL-Plant constraints was applied to single-input single-output (SISO) and multiple-input multipleoutput (MIMO) systems, demonstrating consistent success across both schemes of control. It was also determined that the SFL-Plant constraints do increase the computational demand by 2 to 5 times that of the SFL when unconstrained. However, the difference in absolute time is not so significant, typically an MPC which acted on a system each minute required less than 5 seconds of computation time with inclusion of SFL-Plant constraints. This technique 5 presents the opportunity to use the SFL-MPC with real system constraints with little additional computation effort, where otherwise this may have not been possible. A further advancement in this research is the comparison between the SFL-MPC technique to an MPC with a data-driven model - AutoRegression model with eXogenous input (ARX) – which is widely used in industry. An ARX model was identified for batch supersaturation control using a batch crystallization model of paracetamol in isopropyl alcohol (IPA) in gPROMS Formulated Products as the plant, and an ARX model developed in an industrial software for advanced process control – PharmaMV. The ARX-MPC performance was compared with SFL-MPC performance and it was found that although the ARX-MPC performed well when controlling a process which operated around the point the ARX-MPC was initially identified, the capability of tracking the supersaturation profile deteriorated when larger setpoints were targeted. SFL-MPC, on the other hand, saw some deterioration in performance quantified through an increase in output tracking error, but remained robust at tracking a wide range of supersaturation targets, thus outperforming the ARX-MPC for trajectory tracking control. Finally, single-objective and multi-objective optimization of a batch crystallization process is investigated to build on the existing techniques. Two opportunities arose from the literature review. The first was the use of variable-time decision variables in optimization, as it appears all pre-existing crystallization optimization problems to determine the ideal crystallization temperature trajectory for maximising mean-size are constructed of piecewise-constant or piecewise-continuous temperature profiles with a fixed time step. In this research the timestep was added as a decision variable to the optimization problem for each piecewise continuous ramp in the crystallization temperature profile and the results showed that for the maximisation of mean crystal length in a 300-minute batch simulation, when using 10 temperature ramps each of variable length resulted in a 20% larger mean size than that of 10 temperature ramps, each over a fixed time length. The second opportunity was to compare the performance of global evolution based Nondominated Sorting Genetic Algorithm – II (NSGA-II) with a deterministic SQP optimization method and a further hybrid approach utilising first the NSGA-II and then the SQP algorithm. It was found that for batch crystallization optimization, it is possible for the SQP to converge a global solution, and the convergence can be guaranteed in the shortest time with little compromise using the hybrid 6 method if no information is known about the process. The NSGA-II alone required excessive time to reach a solution which is less refined. Finally, a multi-objective optimization problem is formed to assess the ability to gain insight into crystallization operation when there are multiple competing objectives such as maximising the number weighted mean size and minimizing the number weighted coefficient of variation in size. The insight gained from this is that it is more time efficient to perform single-objective optimization on each objective first and then initialize the multi-objective NSGA-II algorithm with the single-objective optimal profiles, because this results in a greatly refined solution in significantly less time than if the NSGA-II algorithm was to run without initialization, the results were approximately 20% better for both mean size and coefficient of variation in 10% of the time with initialization

Laboratory directed research and development. FY 1995 progress report

This document presents an overview of Laboratory Directed Research and Development Programs at Los Alamos. The nine technical disciplines in which research is described include materials, engineering and base technologies, plasma, fluids, and particle beams, chemistry, mathematics and computational science, atmic and molecular physics, geoscience, space science, and astrophysics, nuclear and particle physics, and biosciences. Brief descriptions are provided in the above programs