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Inclusion of long chain branching (LCB) in polymers is a challenging and important task in any free radical polymerization as LCB influences polymer product quality. In the present case, batch optimization study for the bulk polymerization of vinyl acetate has been considered to find optimal process conditions for imparting LCB in polymer architecture. A theoretical study has been conducted with a validated model to observe the effect of live radical concentration on LCB as this is an important factor for branching in polymer via chain transfer to polymer route. In order to obtain better polymer product in less time at various temperatures, a need was observed to perform a multi-objective optimization study as the selected objectives were conflicting in nature. Owing to the complex nature of moment-based species balance equations and molecular weight distribution function, elitist non-dominated sorting genetic algorithm II (NSGA II), a well-established multi-objective evolutionary algorithm, has been employed as an evolutionary computation method to find out the Pareto optimal solutions. Minimization of polymerization time, maximization of molecular weight and maximization of number average degree of branching (Bn) can be simultaneously achieved, while the solutions were obtained within the experimental range of polydispersity index and weight average molecular weight (Mw) given in the open literature. Results show a wide range of process choices satisfying process objectives and constraints, both in low as well as high temperature regions. [ABSTRACT FROM PUBLISHER]

Tetrabutoxytitanium (TBOT) is a proven catalyst for the esterification step of the polypropylene terepthalate (PPT) polymerization process. Previous studies show that the performance of TBOT is superior in terms of the enhanced degree of polymerization and less processing time to other competitive catalysts. But, interesting observation left was to investigate whether with other process objectives like by-product minimization and controlled growth of desired functional groups, any other catalyst offers better system performance or not. Present study carries out the exercise of searching other catalytic options along with TBOT for the process improvements and gives a detailed process analysis through different sets of optimized operations. A well-validated kinetic model for esterification step of PPT polymerization process and the advanced Real-Coded Nondominated Sorting Genetic Algorithm-II (Real-Coded NSGA-II) optimization routine have been used in this current effort. For process objectives like by-product minimization, TBOT though become a marginal winner, Sn- and Zn-based catalysts compete with each other. Zn-based catalyst is found probably the most suitable catalyst in terms of the overall process performance with excellent by-product minimization (∼10 times better), higher degree of polymerization (∼1.75 times better), and tight quality control (∼5 times better). [ABSTRACT FROM AUTHOR]

Abstract: Multiobjective Pareto optimal solutions for epoxy semi-batch polymerization process are obtained by adapting nondominated sorting genetic algorithm II (NSGA II). The objective is to produce polymer of maximum possible number average molecular weight (Mn) with a specified value of polydispersity index (PDI) and number average molecular weight in minimum possible time. The Mn and reaction time are, therefore, taken as two objectives where the first one is maximized and the second one is minimized. The decision variables are addition profiles of various reactants and the reaction time itself and PDI is treated as a constraint. In another optimization study, the time intervals are also been changed from hourly addition assumptions to equal interval additions for an optimized time frame. Additionally, similar analysis has been performed with a new addition strategy with total additions of reactants very close to available experimental conditions. Sensitivity analysis for estimated kinetic parameters and analysis for stabilization of products are also studied. A validated model, taking care of required physico-chemical aspects of the proposed reaction mechanism, is a prerequisite for this kind of study. [Copyright &y& Elsevier]

Continuous endeavors are going on in many research works to find out the strategy to mathematically model and optimize complex reaction networks in order to maximize the main product and at the same time keeping the reactor dimensions within some acceptable limits. The aim of this work is to provide with a strategy for efficient modeling and optimization of reaction networks for reaction controlled processes. Genetic algorithm (GA) has been used for optimizing complex search spaces with multiple optima. Formation of styrene monomer from the ethylbenzene dehydrogenation, with several by-products in a fixed bed reactor, is taken as an example for this study. Two activation energies are found to be the best in term of maximizing styrene productivity. [Copyright &y& Elsevier]

The epoxy-polymerization process can be better understood by investigating the underlying optimization problem involving a number of conflicting objectives and more than 20 decision parameters. A combination of minimization or maximization of objectives, such as the number average molecular weight, polydispersity index and reaction time, are considered in this paper. The first two objectives are related to the properties of a polymer, whereas the third objective is related to productivity of the polymerization process. The decision variables are addition quantities of various reactants, e.g. the amount of addition for bisphenol-A (a monomer), sodium hydroxide and epichlorohydrin at different time steps (modeled in a semi-batch operation), whereas the satisfaction of all species balance equations is treated as constraints. A multi-objective evolutionary algorithm (the elitist non-dominated sorting genetic algorithm or NSGA-II) is used to obtain a set of non-dominated solutions in a single simulation run. The results show a substantial improvement (with about 300% more productivity) over the benchmark condition (reported by performing a one-time addition of reactants in the beginning in a batch process). Importantly, this study brings out a salient aspect of using an evolutionary approach to multi-objective problem solving. The availability of multiple optimal trade-off solutions allows a process engineer to have salient information about the polymerization process. Changes in the distribution of various polymer species in the course of polymerization process as observed among various Pareto-optimal solutions are identified and explained for this purpose. Such information provide important information about optimal operating conditions corresponding to different trade-offs among objectives, which are otherwise difficult to obtain. The systematic approach of starting from the two-objective problems to capture the essential features of interesting optimal operating conditions to finally solving the three-objective problem associated with the epoxy-polymerization problem in discovering the optimal trade-off interactions should motivate further such studies on other chemical process optimization problems. Overall, this paper demonstrates how fundamental optimization principles can be used systematically and reliably to find optimum operating conditions for complex chemical process operations. [Copyright &y& Elsevier]