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. POLYM. ENG. SCI., 2008. © 2008 Society of Plastics Engineers