MULTIOBJECTIVE OPTIMIZATION OF PROCESS PLANT USING GENETIC ALGORITHM

The optimal design of large-scale process plant is difficult due to the presence of Pareto sets or nondominated solutions. Many conventional and advanced mathematical techniques had been adopted which have their own limitations in solving the complex design problem. In this paper, nondominant-sorted genetic algorithms NSGA and NSGA-II have been adopted for the optimal design of complex Williams–Otto model process plant. The plant consists of a reactor, separation system consisting of heat exchanger, decanter and distillation column. Multiobjective optimization is used to maximize the profit, i.e. the return on investment, to maintain lesser use of costlier raw material and lesser disposal of the waste byproducts. So NSGA-II is employed in this study as an effective replacement for NSGA, classical genetic algorithm, conventional and traditional methods of optimization in solving multiobjective process design problems and to achieve fine-tuning of variables in determining Pareto optimal design parameters. NSGA-II method finding global optimal front has a significant effect on the design of control system for the real time and continuous robust control of complex process plant as each target vector provides proper direction and drives the process to multiobjective optimum conditions.