Multi-Objective Optimization and Multi-Criteria Decision-Making Approach to Design Multi-Tubular Packed-Bed Membrane Reactor in Oxidative Dehydrogenation of Ethane

TL;DR

This paper presents a multi-objective optimization framework using genetic algorithms and decision-making methods to optimize the design and operation of a multi-tubular packed-bed membrane reactor for ethane oxidative dehydrogenation, balancing multiple performance criteria.

Contribution

It introduces a novel combination of NSGA-II with three MCDM methods to optimize and select reactor operating conditions, providing comprehensive trade-off analysis.

Findings

Identified optimal reactor configurations balancing ethylene yield and energy efficiency.

Demonstrated the effectiveness of combined MOO and MCDM methods in reactor design.

Provided decision-makers with a set of Pareto-optimal solutions for practical implementation.

Abstract

Ethylene is a crucial chemical in manufacturing numerous consumer products. In recent years, the oxidative dehydrogenation of ethane (ODHE) technique has garnered significant interest as a means for ethylene production due to its high ethylene selectivity and energy efficiency, the availability of ethane as a feedstock, and catalysts that can improve the performance of the process. This work delves into determining the optimal operating conditions for the ODHE process within a multi-tubular packed-bed membrane reactor by formulating and resolving multi-objective optimization (MOO) problems. The elitist non-dominated sorting genetic algorithm II (NSGA-II) method is employed in conjunction with three different multi-criteria decision-making (MCDM) methods: (i) technique for order of preference by similarity to ideal solution (TOPSIS), (ii) preference ranking on the basis of ideal-average distance (PROBID), and (iii) simple additive weighting (SAW), to solve the MOO problems, obtain the optimal Pareto frontier, and recommend one solution for ultimate implementation. Through this approach, this work elucidates the trade-offs between conflicting objectives, deepens the comprehension of their interrelationships, and underscores the effects of decision variables on the optimization objectives. Consequently, this work provides practitioners with the insights needed for informed decision-making in the ODHE process operation.