Multi-Objective\nOptimization and Multi-Criteria Decision-Making\nApproach to Design a Multi-Tubular Packed-Bed Membrane Reactor in\nOxidative Dehydrogenation of Ethane

Ethylene is a crucial chemical in the manufacturing of\nnumerous\nconsumer products. In recent years, the oxidative dehydrogenation\nof ethane (ODHE) technique has garnered significant interest as a\nmeans for ethylene production due to its high ethylene selectivity\nand energy efficiency, the availability of ethane as a feedstock,\nand catalysts that can improve the performance of the process. This\nwork delves into determining the optimal operating conditions for\nthe ODHE process within a multitubular packed-bed membrane reactor\nby formulating and resolving multiobjective optimization (MOO) problems.\nThe elitist nondominated sorting genetic algorithm II (NSGA-II) method\nis employed in conjunction with three different multicriteria decision-making\n(MCDM) methods: (i) technique for order of preference by similarity\nto ideal solution (TOPSIS), (ii) preference ranking on the basis of\nideal-average distance (PROBID), and (iii) simple additive weighting\n(SAW), to solve the MOO problems, obtain the optimal Pareto frontier,\nand recommend one solution for ultimate implementation. Through this\napproach, this work elucidates the trade-offs between ethane conversion,\nselectivity, and carbon dioxide production objectives, deepens the\ncomprehension of their interrelationships, and underscores the effects\nof decision variables on the optimization objectives. The Pareto optimal\nset for optimizing C<sub>2</sub>H<sub>4</sub> and CO<sub>2</sub> annual\nproduction has the range of 4.78–80.14 kton and 0.195–13.24\nkton, respectively. Consequently, this work provides practitioners\nwith the insights needed for informed decision-making in ODHE process\noperation.