Multiobjective Optimization of an Industrial Nylon-6 Semi Batch Reactor Using the a-Jumping Gene Adaptations of Genetic Algorithm and Simulated Annealing


The elitist nondominated sorting genetic algorithm (NSGA-II) and multiobjective simulated annealing (MOSA) with the robust fixed-length jumping gene adaptation (aJG) are used to solve three computationally intensive multiobjective optimization problems for an industrial semi batch nylon-6 reactor. In Problems 1 and 2, the batch time and the final concentration of the undesirable side-product (cyclic dimer) are minimized while maintaining desired values of the degree of polymerization of the product and the monomer conversion (monomer conversion is maximized as a third objective in Problem 3). The histories of two decision variables, pressure [or vapor release rate] and jacket fluid temperature, are used to obtain the Pareto optimal fronts. The study predicts considerable improvement over earlier results when (i) a single-stage steam jet ejector is used to create subatmospheric pressures in the reactor, (ii) when the jacket fluid temperature is taken as a function of time, and (iii) when some amino caproic acid (from the depolymerization of scrap nylon-6) is added to the feed.