Improving the Performance of a Catalytic Membrane Reactor via Stochastic Optimization

Abstract

Dehydrogenation of ethyl benzene and hydrogenation of nitrobenzene are coupled in the catalytic shell and tube membrane reactor to enhance the conversion and yield of the dehydrogenation reaction. The reactor system needs to be optimized to achieve the maximum benefit. Six process variables are selected as decision variables, which are; ethyl and nitrobenzene molar flow rate, pressure and temperature on shell and tube side. The flow rate of ethyl benzene can be considered as the effective variable on the nitrobenzene conversion and styrene yield. Optimization technique is the powerful tool to generate several new designs and sets of operating conditions. This reduces the risk of experimental runs and the expanded cost on the design and operation. The optimal operating conditions could enhance the yield of styrene within the range of (51 to 99.6%) and conversion of nitrobenzene within range of (35.7 to 79.6%) compared to the previous works which were within range of (49 to 98%) for styrene yield and (21 to 79%)  for nitrobenzene conversion at the same range of operating conditions. For highly nonlinear membrane reactor, the global stochastic genetic optimization algorithm has been found more suitable than the deterministic methods. The reliability of the search can be increased by the adaption of the genetic operators
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