Abstract
A comprehensive optimization study on a simulated countercurrent moving bed chromatographic reactor (SCMCR) is reported in this article for oxidative coupling of methane (OCM) reaction. The selection of the operating parameters such as the switching time, make-up feed rate, methane to oxygen ratio in feed, length of columns and flow rates in different sections are not straightforward in an SCMCR. In most cases, conflicting requirements and constraints govern the optimal choice of the decision (operating or design) variables. An experimentally verified mathematical model was selected to optimize the performance of the SCMCR for OCM. A few multi-objective optimization problems were solved for both existing setup and at design stage. The optimization was performed using a state-of-the-art AI-based non-traditional optimization technique, non-dominated sorting genetic algorithm with jumping genes (NSGA-II-JG), which resulted in Pareto optimal solutions. It was found that the performance of the SCMCR could be improved significantly under optimal operating conditions.
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