Multiobjective optimization of an industrial nylon-6 semibatch reactor system using genetic algorithm

Abstract

Multiobjective Pareto optimal solutions for three different grades of nylon-6 produced in an industrial semibatch reactor are obtained by using the adapted Nondominated Sorting Genetic Algorithm (adapted NSGA). The two objective functions minimized are the total reaction time and the concentration of undesirable cyclic dimer in the product, while simultaneously attaining desired values of the monomer conversion and the number average chain length. The control variables used are the fractional valve opening f(t) and the jacket fluid temperature T J . The study shows a marked improvement over current industrial operation. It is found that the optimal values of the cyclic dimer concentration in the product are worse (higher) when the reactorcontrol valve system is studied than when the reactor is considered alone. This is because the control valve leads to additional constraints. The technique used is quite general and can be used to study other reactor systems as well.

Applications of Genetic Algorithm in Polymer Science and Engineering

“…1). For a two-objective function problem, e.g., the Pareto set is the locus of (equally good) optimal points so that if one moves from any [12] ; broken lines: [9] ]. Subscript, ref., indicates values being used in industry before changeover to near-optimal conditions.…”

“…Gupta and Gupta [12] extended this work on the industrial nylon-6 reactor system to consider the multiobjective optimization of the reactor-cum-control valve assembly. They considered the fractional opening of the control valve as one of the decision variables (again, a function of time), instead of the rate of release of vapor from the reactor.…”

Optimisation of reverse osmosis based wastewater treatment system for the removal of chlorophenol using genetic algorithms

“…In many applications, the use of GA has yielded better results in optimisation in comparison to other conventional methods [6]. For instance, GAs have been used extensively in different areas of chemical engineering process design and operation, such as, distillation system [7], semi-batch reactor [8], multi-phase catalytic reactor (hydrogenation reaction system) [9], microchannel reactor (emerged as a novel technology for the synthesis of liquid hydrocarbons applications) [10] and steam reforming of hydrocarbons for the generation of hydrogen and synthesis gas [11]. Also, Fang et al [12] have combined an integrated Neural Network (NN) dynamic model and GA approach to optimise the performance of a full-scale municipal wastewater treatment plant with substantial influent fluctuations.…”

Multi‐Objective Optimization of a Batch Copoly(ethylene‐polyoxyethylene terephthalate) Reactor Using Different Adaptations of Nondominated Sorting Genetic Algorithm

“…Bhaskar et al18 have reviewed the variety of chemical engineering related multi‐objective optimization problems that have been solved using NSGA‐I and other nonevolutionary techniques. Several parallel studies on multi‐objective optimization of various polymerization reactors, namely, Nylon 6,19,20 poly(methyl methacrylate) (PMMA),21 PET,22 polystyrene (PS),23,24 styrene acrylonitryl (SAN) copolymer25 have also been reported in the literature. It is always desirable to obtain the best quality product (i.e., product having the least impurities) with a minimum production time (maximum throughput).…”