(307f) Multiobjective Optimization of Autothermal Catalytic Membrane Reactor Using Genetic Algorithm
Membrane reactors are suggested to combine reaction with separation to enhance the conversion and yield by overcoming the thermodynamics limitation. Many variables affect the performance of such reactors.Therefore, reactor system needs to be optimized to achieve the maximum benefit. Abo- ghander (2010) studied the optimization of autothermal membrane reactor coupling the dehydrogenation of ethylbenzene to styrene with the hydrogenation of nitrobenzene to aniline. The total number of the decision variables considered in the optimization problem was 12, representing a set of operating and design parameters.The problem was solved numerically by two deterministic multiobjective approaches: the Normalized Normal Constraint (NNC) method and Normal Boundary Intersection (NBI) method.However, the membrane reactor for these reactions is highly non-linear and in addition Abo-ghander (2010) used the dynamic mode to correlate the objectives with decision variables. For highly nonlinear system, the stochastic optimization algorithm has been found better than the deterministic approachesed by Abo-ghander(2010). Deterministic search techniques, use characteristics of the problem(e.g.,Jacobian, Hessian) to determine the next sampling point while stochastic search techniques do not need such information.Instead,the next step in stochastic methods would be determined based on statistical sampling rules rather than aset of rigorous deterministic. Furthermore steady state or static formulations that correlate the objectives with decision variables are more reliable and accurate compared the dynamic mode with the process of long cycle time of reaction. Accordingly, the focus of this work is to extend and improve the optimization effort of Abo-ghander(2010)by implementing stochastic optimization using genetic algorithm.Reducing the number of the decision variables to 6 instead of 12 used by Abo-ghandar(2010) and using steady state(static) correlations that correlate the objectives; conversion of nitrobenzene and yield of styrene ,with the decision variables; ethyl and nitro benzene flow rate,pressure and temperature on the shell and tubes side. The noticeable improvement has been found where the yield of styrene has increased within range of 74- 100% compared to 49-98% obtained by the algorithms of the optimization used by Abo-ghander(2010), and the finidings will be discussed in this presentation.
Abo-ghander, N.S.M,”Coupling Dehydrogenation of Ethylbenzene with Hydrogenation of Nitrobenzene in an Autothermal Catalytic Membrane Reactor”, Ph.D thesis, Chemical and Biological Engineering Department, University of British Columbia, (2010).