Evolutionary algorithms (EAs), which are basically the population-based search algorithms, are becoming increasingly popular for solving highly nonlinear and complex optimization problems (both the single-objective and multiobjective problems). Multiobjective Differential Evolution (MODE), which is an extension of Differential Evolution (DE) to multiobjective optimization, has been successfully applied to a few chemical process industrial problems. This article presents multiobjective optimization of p-xylene oxidation in the manufacturing process of industrial Purified Terephthalic Acid (PTA) oxidation using MODE, its improved strategy, i.e., Elitist-Multiobjective Differential Evolution (E-MODE), and NSGA-II. E-MODE algorithm includes the concept of elitism as present in NSGA-II. Four industrially important cases are studied from the multiobjective optimization point of view. The effect of six decision variables identified, namely, catalyst concentration, vent oxygen content, % water in solvent, feed xylene rate, temperature of reactor, and total feed rate, on the formulated objective function for the four cases is studied. The corresponding Pareto optimal sets (i.e., sets of equally good nondominated solutions) are obtained. All decision variables, corresponding to the Pareto optimal solutions, are plotted against each of the objectives. Comparison among the Pareto optimal fronts of four cases is also carried out. Pareto solutions obtained in this study (using MODE, E-MODE, and NSGA-II) lie on same (Global) Pareto front. However, MODE is able to cover a better range than E-MODE and NSGA-II. The results obtained are useful to a plant engineer for possible improvement in the process design decisions.
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