Simulation and Multiobjective Optimization of a Continuous Biochemical Reactor Using Multilayer Modeling Technique

Biochemical reactors are essential unit operations in a wide variety of biotechnological process.As compared to  conventional  chemical reactors, bioreactors present unique modeling and control challenges due to complexity of the underlying biochemical reactions.  The biological processes are inherently very nonlinear and have frequently changing optimum operating conditions. Therefore,optimization of biochemical reactor process requires a mathematical model that describes and predicts  the process behavior. The simulation and optimization of the present biochemical reactor which used substrate  (glucose) was studied. Multilayer modeling technique was applied.The first layer is hybrid model was consist the framework of the dynamic mass balance equations supplemented with the sub models of kinetic reaction.The optimum problem  represents the second modeling layer. To overcome the limitation of the experimental data, the accurate simulated model was used as a powerful tool to generate data which construct the objective functions of the optimization problem .The objective of the optimization  technique was to maximize the conversion and minimize the cycle time of reaction. The decision (effective) variables  were the dilution rate and the inlet concentration of the substrate. Optimization  search can generate several new designs  and sets of operating conditions.This can reduce the number of experimental  runs and the cost consumed for design and operation.Also it limits the best range of the operating conditions which improve the efficiency the reactor. The yield of the biomass increased by 43% at the optimum conditions. The multiobjective stochastic search by genetic algorithm with adaptive operators was the best for  the present nonlinear interacted biochemical reactor.