Optimal Scheduling for Chemical Processes and its Integration with Design and Control

Optimal scheduling is an active area of research as the economics of many chemical processes is affected to a great extent with the optimality of schedules of their operations. Effective use of resources and their capacities is paramount in order to achieve optimal operations. Manual and heuristics-based approaches used for scheduling have their limitations which inhibit the chemical process industries to achieve economically attractive operations. One such sector is the analytical services industries and success of companies in this sector highly relies on the effective scheduling of operations as large numbers of samples from customers are received, analyzed and reports are generated for each sample. Therefore, it is extremely important to efficiently use all the various resources (labor and machine) for such facilities to remain competitive. This study focuses on the development of an algorithm to schedule operations in an actual large scale analytical services plant using models based on multi-commodity flow (MCF) and integer linear programming (IP) techniques. The proposed scheduling algorithm aims to minimize the total turnaround time of the operations subject to capacity, resource and flow constraints. The basic working principles of the optimization-based algorithm are illustrated with a small representative case study, while its relevance and significance is demonstrated through another case study of a real large scale plant. In the latter case study, the algorithm’s results are compared against historical data and results obtained by simulating the current policy implemented in the real plant, i.e., first-come first-served. Along with scheduling, many chemical processes require the optimization of other aspects that play major part in the process economics, e.g. design and control. An important section of the chemical process industry produces various grades of products (multi-product) and the scheduling of the production of these grades along with optimal design and control play important roles in the economy of the operations. As part of this research study, a new methodology that can address three aspects of the economy of the multiproduct processes together; i.e. simultaneous scheduling, design and control, has been developed. A mixed integer non linear programming (MINLP) optimization framework has been formulated, which aims to simultaneously evaluate optimal design, steady state operating conditions for each grade as a part of design, optimal tuning parameters for the controllers, optimal sequence of production of various grades of product and optimal smooth transitions between the grades. This is achieved via minimization of overall cost of the operation. The proposed methodology takes into account the influence of disturbances in the system by the identification of the critical frequency from the disturbances, which is used to quantify the worst-case variability in the controlled variables via frequency response analysis. The uncertainty in the demands of products has also been addressed by creating critical demand scenarios with different probabilities of occurrence, while the nominal stability of the system has been ensured. Two case studies have been developed as applications of the methodology. The first case study focuses on the comparison of classical semi-sequential approach against the simultaneous methodology developed in this work, while the second case study demonstrates the capability of the methodology in application to a large-scale nonlinear system

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

Integration of process design and control: A review

There is a large variety of methods in literature for process design and control, which can be classified into two main categories. The methods in the first category have a sequential approach in which, the control system is designed, only after the details of process design are decided. However, when process design is fixed, there is little room left for improving the control performance. Recognizing the interactions between process design and control, the methods in the second category integrate some control aspects into process design. With the aim of providing an exploration map and identifying the potential areas of further contributions, this paper presents a thematic review of the methods for integration of process design and control. The evolution paths of these methods are described and the advantages and disadvantages of each method are explained. The paper concludes with suggestions for future research activities

Free-Radical Polymerization of Polystyrene Using Microreaction Technology

La technologie de microréaction chimique est un sous domaine de l'ingénierie des procédés qui se concentre sur l'étude des réactions chimiques réalisées à l'intérieur de systèmes miniaturisés communément appelé microréacteurs. Les microréacteurs sont essentiellement des mélangeurs statiques miniaturisés fonctionnant en mode continu pour écoulements entraînés par la pression. Ils peuvent être constitués d'un ou plusieurs microcanaux parallèles de longueurs différentes de l'ordre des micromètres. Les microréacteurs se différencient fortement des réacteurs de synthèse traditionnels par plusieurs caractéristiques clés reliés à l’intensification des procédés telles que des plus élevés gradients de concentration et température, et une réduction du temps de mélange, une capacité de transfert de chaleur plus élevé, une augmentation de la surface d’échange surface/volume, etc. Les microréacteurs attirent de ce fait de plus en plus l’attention de la communauté scientifique qui y voit l’opportunité d’accéder à des voies de synthèse jusqu’alors inaccessibles dans les réacteurs classiques. Il existe actuellement de nombreux microréacteurs disponibles commercialement conçus pour atteindre des conditions de haute pression et température. Ces microréacteurs peuvent être produits en masse par des techniques de fabrication de pointe. Par conséquent, de nouvelles applications sont envisagées afin de les utiliser comme outils de production alternatifs dans différents domaines de l'ingénierie. Un exemple serait l’utilisation de cette technologie pour la production de polymères. Les avantages présentés par les microréacteurs en termes de contrôle de température et conditions de mélange sont non négligeables lorsque l’on considère les réactions de polymérisation, généralement fortement exothermiques et extrêmement sensibles en termes de mélange des réactifs. Malgré cela, la technologie de microréaction n'a été que très peu utilisée dans les processus de polymérisation en continu. Pour faire l’évaluation du vrai potentiel de ce type de technologie la caractérisation hydrodynamique du microréacteur est une étape essentielle. Dans ce contexte, la distribution du temps de séjour (DTS) est un outil majeur pour caractériser l’hydrodynamique d’un réacteur chimique quelque soit l’échelle. La DTS renseigne sur le comportement d’un écoulement, sur les processus de mélange et leur interaction avec la cinétique des réactions survenant à l'intérieur d’un réacteur. Le temps passé par une molécule sous les conditions de réaction dans un système aura une incidence sur la probabilité de cette dernière de réagir. De ce fait, la mesure, l'interprétation et la modélisation de la DTS sont des aspects importants pour la prédiction de la composition finale d’un système impliquant une réaction chimique. On note toutefois, que dans la plupart des cas sur la technologie de microréaction, l’étape de caractérisation a été menée sur des équipements de laboratoire destinés à des fins de visualisation et non conçus pour l’opération dans des conditions de haute pression et température. Seuls quelques rapports expérimentaux existent sur la caractérisation des unités commerciales de microréaction et les informations sur leur utilisation en tant que réacteur de polymérisation sont rares. On comprend de ce fait pourquoi des études de faisabilité sont nécessaires afin de déterminer dans quelle mesure la technologie de microréaction peut être appliquée pour les réactions polymérisation. Pour de telles applications, la distribution du débit total dans chacun des microcanaux peut affecter le transfert de chaleur et l'efficacité du mélange modifiant les conditions de réactions pendant la polymérisation, jouant ainsi rôle sur les propriétés finales du produit. L'objectif général de ce projet est d’évaluer la faisabilité d’utiliser la technologie de microréaction pour la production en mode continu de polymère tout en développant une meilleure compréhension de l'écoulement et des caractéristiques de mélange pour la conception de microréacteur le plus performant. La réaction de polymérisation du monomère styrène en utilisant des initiateurs de peroxyde sera utilisée comme système de réaction. La méthodologie de ce projet est planifiée pour s’adresser à l’hydrodynamique et à la performance du mélange, à la capacité de transfert de chaleur et au niveau de conversion de polymères obtenus dans deux microréacteurs ayant des mécanismes de micromélange différents et des échangeurs de chaleur intégrés. Les mécanismes de mélange considérés sont le mécanisme de division-et-recombinaison d’écoulements (SAR par ses sigles an anglais: split-and-recombination), et la subdivision d’écoulement par des structures interdigitaux; et ce qui sont les deux principes de mélange les plus fréquemment utilisés pour les microréacteurs commerciaux. -------- Microreaction technology is presently a well established subfield of the chemical microprocess engineering that focuses on the study of chemical reactions conducted inside of the structured channels of miniaturized flow vessels commonly referred as microreactors. Microreactors are essentially miniaturized static mixers that operate in continuous mode under pressure driven flow. They can be composed of single or multiple parallel microchannels of different lengths with typical cross sections in the micrometer range. The gradients of the physical properties of a material are increased when its linear dimensions are reduced and some of theses gradients, e.g. temperature and concentration, are particularly important for the control of chemical engineering processes. Consequently, microreaction technology has drawn great attention from the process engineering community during the last decades due to their theoretical benefits in terms of transport phenomena and due to their new envelope of reaction conditions otherwise inaccessible in macroscopic equipment. Presently, there is a large repertoire of commercially available microreactors designed and built for mechanical robust operation which can be mass produced by state-of-the-art manufacturing techniques. Therefore, new applications are being sought for existing microreaction equipment trying to incorporate them as alternative production tools into different fields of engineering. In this context polymer reaction engineering applications could fully exploit the benefits of microreaction conditions in terms of temperature control and fast mixing since polymerization reactions are usually highly exothermic and extremely sensitive to the level of mixing of the reactants. Nevertheless microreaction technology has not been extensively applied in continuous polymerization processes. Residence time distribution (RTD) theory is a major tool for reactor characterization at any scale level that provides substantial insight of flow behavior and mixing processes and their interaction with the kinetics of reactions occurring inside a flow vessel. The time a molecule spends under reaction conditions in a reactive system will affect its probability of reacting; therefore the measurement, interpretation and modeling of RTD are important aspects for the prediction of the final composition of the system. However, for the most part such type of characterization has been conducted on laboratory equipment intended for visualization purposes and not for more demanding operating conditions. Only few experimental reports exist on the characterization of commercial microreaction units and information about their capabilities as polymerization reactors is not available at all. In this context feasibility studies are necessary in order to determine the extent of applicability of microtechnology in the polymer reaction engineering field. For such applications, the flow distribution in microchannel networks can affect the heat transfer and mixing efficiency at the microscale modifying the working conditions accessible during polymerization which will consequently affect the final properties of the product. The general objective of this project is to test the feasibility of individual microreaction units to be used for continuous polymer production and to develop a better understanding of the flow and mixing characteristics of specific microreactor commercial designs. The peroxide initiated polymerization of styrene monomer will be used as reaction system. The methodology of this project is structured as to address the hydrodynamic and mixing performance, the heat transfer capabilities and the level of conversion of polymer achieved on two microreactors featuring different micromixing mechanisms with integrated heat exchangers. The mixing mechanisms considered are the split-and-recombination (SAR) mechanism and the multilamination of flow by means of interdigital structures which are two mixing principles most frequently featured by commercial microreactors

Analysis of rheological properties and molecular weight distributions in continuous polymerization reactors

This work explores the possibility of exploiting structure-property relationships to manufacture tailor-made polymers with target end-use properties. A novel framework which aims to improve upon current industrial practices in polymerization process and product quality control is proposed. The strong inter-relationship between the molecular architecture and rheological properties of polymers is the basis of this framework. The melt index is one of the most commonly used industrial measures of a polymer's processibilty. However, this single-point non-Newtonian viscosity is inadequate to accurately reflect the polymer melt's flow behavior. This justifies monitoring the entire viscosity-shear rate behavior during the polymerization stage. In addition, the crucial role played by the polymer melt's elastic characteristics is not reflected in it's shear viscosity and so elasticity meaurements are also warranted. In this study, rheological models available in the open literature are utillized to demonstrate these critical issues at industrially relevant operating conditions. The observations made are also compared with published experimental results and found to be qualitatively similar. Two case studies are presented. The first one is the free-radical solution polymerization of styrene with binary initiators in a cascade of two CSTRs. In the second case, the solution polymerization of ethylene in a single CSTR with a mixture of two single-site transition metal catalysts is considered. The feasibility of the proposed framework to tailor the product's MWD, irrespective of the underlying reactor configuration or kinetic mechanism, is demonstrated via steady state simulations. Relative gain analysis reveals the non-linearity and interactions in the control loops. Although the main contributions of this study primarily deal with the viscoelastic behavior of linear homopolymers, potential extensions to systems involving polymers with small amounts of long chain branching or the control of other end-use properties are also discussed

Novel Methodologies in State Estimation for Constrained Nonlinear Systems under Non-Gaussian Measurement Noise & Process Uncertainty

Chemical processes often involve scheduled/unscheduled changes in the operating conditions that may lead to non-zero mean non-Gaussian (e.g., uniform, multimodal) process uncertainties and measurement noises. Moreover, the distribution of the variables of a system subjected to process constraints may not often follow Gaussian distributions. It is essential that the state estimation schemes can properly capture the non-Gaussianity in the system to successfully monitor and control chemical plants. Kalman Filter (KF) and its extension, i.e., Extended Kalman Filter (EKF), are well-known model-driven state estimation schemes for unconstrained applications. The present thesis initially performed state estimation using this approach for an unconstrained large-scale gasifier that supports the efficiency and accuracy offered by KF. However, the underlying assumption considered in KF/EKF is that all state variables, input variables, process uncertainties, and measurement noises follow Gaussian distributions. The existing EKF-based approaches that consider constraints on the states and/or non-Gaussian uncertainties and noises require significantly larger computational costs than those observed in EKF applications. The current research aims to introduce an efficient EKF-based scheme, referred to as constrained Abridged Gaussian Sum Extended Kalman Filter (constrained AGS EKF), that can generalize EKF to perform state estimation for constrained nonlinear applications featuring non-zero mean non-Gaussian distributions. Constrained AGS-EFK uses Gaussian mixture models to approximate the non-Gaussian distributions of the constrained states, process uncertainties, and measurement noises. In the present abridged Gaussian sum framework, the main characteristics of the overall Gaussian mixture models are used to represent the distributions of the corresponding non-Gaussian variable. Constrained AGS-EKF includes new modifications in both prior and posterior estimation steps of the standard EKF to capture the non-zero mean distribution of the process uncertainties and measurement noises, respectively. These modified prior and posterior steps require the same computational costs as in EKF. Moreover, an intermediate step is considered in the constrained AGS-EKF framework that explicitly applies the constraints on the priori estimation of the distributions of the states. The additional computational costs to perform this intermediate step is relatively small when compared to the conventional approaches such as Gaussian Sum Filter (GSF). Note that the constrained AGS-EKF performs the modified EKF (consists of modified prior, intermediate, and posterior estimation steps) only once and thus, avoids additional computational costs and biased estimations often observed in GSFs. Moving Horizon Estimation (MHE) is an optimization-based state estimation approach that provides the optimal estimations of the states. Although MHE increases the required computation costs when compared to EKF, MHE is best known for the constrained applications as it can take into account all the process constraints. This PhD thesis initially provided an error analysis that shows that EKF can provide accurate estimates if it is constantly initialized by a constrained estimation scheme such as MHE (even though EKF is unconstrained state estimator). Despite the benefits provided by MHE for constrained applications, this framework assumes that the distributions the process uncertainties and measurement noises are zero-mean Gaussian, known a priori, and remain unchanged throughout the operation, i.e., known time-independent distributions, which may not be accurate set of assumptions for the real-world applications. Performing a set of MHEs (one MHE per each Gaussian component in the mixture model) more likely become computationally taxing and hence, is discouraged. Instead, the abridged Gaussian sum approach introduced in this thesis for AGS-EKF framework can be used to improve the MHE performance for the applications involving non-Gaussian random noises and uncertainties. Thus, a new extended version of MHE, i.e., referred to as Extended Moving Horizon Estimation (EMHE), is presented that makes use of the Gaussian mixture models to capture the known time-dependent non-Gaussian distributions of the process uncertainties and measurement noises use of the abridged Gaussian sum approach. This framework updates the Gaussian mixture models to represent the new characteristics of the known time-dependent distribution of noises/uncertainties upon scheduled changes in the process operation. These updates require a relatively small additional CPU time; thus making it an attractive estimation scheme for online applications in chemical engineering. Similar to the standard MHE and despite the accuracy and efficiency offered by the EMHE scheme, the application of EMHE is limited to the scenarios where the changes in the distribution of noises and uncertainties are known a priori. However, the knowledge of the distributions of measurement noises or process uncertainties may not be available a priori if any unscheduled operating changes occur during the plant operation. Motivated by this aspect, a novel robust version of MHE, referred to as Robust Moving Horizon Estimation (RMHE), is introduced that improves the robustness and accuracy of the estimation by modelling online the unknown distributions of the measurement noises or process uncertainties. The RMHE problem involves additional constraints and decision variables than the standard MHE and EMHE problems to provide optimal Gaussian mixture models that represent the unknown distributions of the random noises or uncertainties along with the optimal estimated states. The additional constraints in the RMHE problem do not considerably increase the required computational costs than that needed in the standard MHE and consequently, both the present RMHE and the standard MHE require somewhat similar CPU time on average to provide the point estimates. The methodologies developed through this PhD thesis offers efficient MHE-based and EKF-based frameworks that significantly improve the performance of these state estimation schemes for practical chemical engineering applications

Precise characterization and modeling of particle morphology development in emulsion polymerization.

256 p.This PhD aimed at paving the way to process optimization and on-line control of particle morphology in emulsion polymerization process. The bottleneck in achieving this goal is the lack of proper device for on-line monitoring of particle morphology. Therefore, the alternative is using a mathematical model as a soft sensor in on-line monitoring. . In this regard, the model developed by Hamzehlou et al.1 is the most promising possibility. The model needs to be capable of describing the evolution of the particle morphology during the polymerization as well as being sensitive to detect the effect of process variables on morphology changes. To evaluate the capacity of mathematical model on prediction of particle morphology development, composite polymer-polymer particle latexes were synthesized in a two-step seeded semi-batch emulsion polymerization by polymerization of more hydrophilic co-monomers (styrene/n-butyl acrylate) in the second stage of polymerization using a more hydrophilic seed of poly (methyl methacrylate-co-n-butyl acrylate). According to thermodynamics, the equilibrium morphology for the studied cases was ¿inverted core-shell¿ while in all synthesized cases in this thesis; kinetically meta-stable morphologies were achieved due to determining effect of radical concentration profile on the development of the particle morphology. The presented model was modified to account for radical concentration profile and the effect of different reaction variables to alter the movement of synthesized clusters at the exterior zone of the particles toward to the equilibrium position in the center of the particles was studied. It was recognized that although the combination of different characterization techniques can provide reliable knowledge about the particle morphology development, it was difficult to reach the conclusion on the effect of process variables on the morphology changes in some cases. To overcome this limit, a method for the precise quantitative 3D characterization of polymer-polymer composite waterborne particles based on high angular dark field -scanning transmission electron microscopy (HAADF-STEM) coupled with image reconstruction is presented. The information about the morphology gathered by this technique revealed mechanistic features on the development of the particle morphology that could not be captured by the conventional TEM images and hence it allowed upgrading the mathematical model. All overall, the upgraded model provides a better prediction of the effect of process variables on the morphology of composite polymer particles and this opens the way to use the model in optimization and on-line control strategies. (1) Hamzehlou, S.; Leiza, J. R.; Asua, J. M. A New Approach for Mathematical Modeling of the Dynamic Development of Particle Morphology. Chem. Eng. J. 2016, 304, 655-666.Polyma

Evolution of particle size distribution in suspension polymerisation reactions

Suspension polymerisation processes are commercially important for the production of polymer beads having wide applications. Polymers produced by suspension polymerisation can be directly used for particular applications such as chromatographic separations and ion-exchange resins. Particle Size Distribution (PSD) may appreciably influence the performance of the final product. Therefore, the evolution of PSD is a major concern in the design of a suspension polymerisation process. In this research, methyl methacrylate (MMA) has been used as a model monomer. A comparative study of MMA suspension polymerisation and MMNwater dispersion was carried out, for the first time, to elaborate the evolution of mean particle size and distribution. Polyvinyl alcohol (PVA) and Lauroyl Peroxide (LPO) have been used as stabiliser and initiator, respectively. Polymerisation experiments were carried out using a 1-litre jacketed glass reactor equipped with a turbine impeller and a condenser. The stabiliser, initiator and chain transfer concentrations, inhibitor concentration and type, reaction temperature, impeller speed, and monomer hold up were used as variables. A mathematical model was developed to predict the kinetics of polymerisation as well as the evolution of PSD by population balance modelling. The experimental results were compared with the model predictions. From the comprehensive experimental results, the characteristic intervals of a typical suspension polymerisation were realised as: 1) Transition stage during which PSD narrows dramatically and drop size decreases exponentially due to higher rate of drop break up in comparison with drop coalescence . _ until a steady state is reached. The importance, and even the existence, of the transition stage have been totally ignored in the literature. The results indicate that increasing the impeller speed, and PV A concentration will lead to a shorter transition period. Also increasing the rate of reaction, via increasing initiator concentration, and reaction temperature will shorten this period. ABSTRACT 2) Quasi steady-state stage during which the rate of drop break up and drop coalescence are almost balanced leading to a steady-state drop size and distribution. The occurrence of this stage is conditional. Low impeller speed and PV A concentration may remove the quasi steady-state stage completely and drops may start growing considerably after a sharp decrease in size during the transition stage. 3) Growth stage during which the rate of drop break up considerably falls below the rate of drop coalescence due to the viscosity build up in drops leading to drop enlargement and PSD broadening. Results show that the onset of the growth stage may not be fixed and it depends on the balance of the forces acting on drops. The onset of the growth stage in terms of time was advanced with decreasing stirring speed and PV A concentration and increasing monomer hold up. Under a static steady state, which is formed when a high concentration of PV A is used, there is almost no growth. 4) Identification stage during which a solid-liquid suspension is attained and the PSD and mean particle size remain unchanged afterwards. The onset of this stage appears to be fairly constant for different formulations. The developed model could fairly predict the rate of polymerisation. It was also capable of predicting the evolution of particle size average and distribution qualitatively in the course of polymerisation. The results can be used as a guideline for the control of particle size and distribution in suspension polymerisation reactors. A more quantitative exploitation of the model has been left for a future research.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Morphological study of composite latex particles and water interaction with polymer chains

The primary purpose of this research was the morphological study of composite polymer (2 or more) nano-particles in an aqueous dispersed system. This included synthesis, characterization and mathematical/numerical modeling. For a composite latex particle, the morphology can play an important role in determining the final application properties, and the structure depends on many factors which are active during and after polymerization. Among these are the penetration of oligomeric chains into the latex particles, mixing of the two composite polymers, and eventual phase separation of such mixtures. These actions depend upon the materials used in the polymerization and the process by which the reaction is carried out. This work involved both the common styrene/acrylic family of monomers as well as the newer polyurethane/acrylic hybrid system. The properties of the second stage polymers such as glass transition temperature, chain length and the polarity were studied and their impacts on the phase separation process were evaluated. For the polyurethane-acrylic hybrid latex system, the phase distribution of the two polymer components in these composite particles was characterized for the first time. Hydrogen bonding between the polymer chains was found to limit the diffusion of polymer chains and restrict phase separation towards the equilibrium morphology. A numerical model of the kinetics of the phase separation process applicable during and after polymerization was also developed. The Cahn-Hilliard theory was applied for this simulation to account for the new interfaces formed during phase separation. Water interaction with polymer chains also became an important aspect of our study. Different states of water can exist simultaneously within a polymeric material, and the physical properties of the material will change depending upon these different states of water

ALIPHATIC SILICA‐EPOXY SYSTEMS CONTAINING DOPO‐BASED FLAME RETARDANTS, BIO‐WASTES, AND OTHER SYNERGISTS

Most industrial applications require polymer‐based materials showing excellent fire performances to satisfy stringent requirements. No‐dripping and self‐extinguishing hybrid silica‐epoxy composites can be prepared by combining tailored sol‐gel synthesis strategies with DOPO‐based flame retardants, bio‐wastes, and other synergists. This approach allows for achieving V‐0 rating in UL‐94 vertical flame spread tests, even using a sustainable route, aliphatic amine as hardener, and low P loadings