Multiobjective Optimization Ofautothermal Catalytic Membrane Reactor using Genetic Algorithm

Abstract

Membrane reactors combined reaction with separation to increase the conversion. Dehydrogenation of ethyl benzene and hydrogenation of nitrobenzene was coupled in the catalytic shell and tubes membrane reactor, so the conversion and yield of the limited dehydrogenation reaction ethyl benzene can be significantly enhanced. the reactor system needs to be optimized to achieve the maximum benefit. the optimization problem of the present work was to maximize the conversion of the nitrobenzene and the yield of the styrene. Six decision (process) variables were studied which are; ethyl & nitro benzene flow rate, pressure and temperature on shell and tubes side .The flow rate of nitro benzene was the effective variable on the conversion of nitrobenzene while the flow rate of ethyl benzene was to be considered as the effective variable on the styrene yield. the mathematical correlations based optimization could be implemented to find the optimum parameters. Optimization technique was the powerful tool to generate several new designs and sets of operating conditions. This reduces the number of experimental runs and the consumed cost for the design and operation. the present optimization search introduced the following improvements to the reactor process:-Reduced the number of manipulated (decision) variables that effecting on the efficiency of the reactor to 6 instead of 12 which considered previously. These eliminate the complexity in the optimization problem and reduced the computing time.-Yield of styrene could be obtained within the range of (74 to100%) compared to the previous work which was within of (49 to 98%) for the same operating conditions. the success of the optimization search was due to:-Good formulation of the steady state objective functions since the objectives were correlated directly to the decision variables. the static formula was more accurate than the dynamic mode for the process of long cycle time of reaction.-For highly nonlinear membrane reactor, the stochastic optimization algorithm was better than the deterministic methods used by the previous work which were limited and inaccurate for nonlinear interacting process. in the present work, the Genetic algorithm was the best stochastic technique and global solution for nonlinear interacting membrane reactor process. the accuracy of Genetic algorithm can be increased by adaption the operators of the Genetic algorithm. Keywords: Chemical Reaction, Genetic Algorithm, Membrane Reactor, Optimization.

Department(s)

Chemical and Biochemical Engineering

International Standard Book Number (ISBN)

978-161839723-2

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 American Institute of Chemical Engineers, All rights reserved.

Publication Date

01 Jan 2011

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