Dynamic optimization of membrane dual-type methanol reactor in the presence of catalyst deactivation using genetic algorithm

This paper presents a study on optimization of a membrane dual-type methanol reactor in the presence of catalyst deactivation. A theoretical investigation has been performed in order to evaluate the optimal operating conditions and enhancement of methanol production in a membrane dual-type methanol reactor. A mathematical heterogeneous model has been used to simulate and compare the membrane dual-type methanol reactor with conventional methanol reactor. An auto-thermal dual-type methanol reactor is a shell and tube heat exchanger reactor which the first reactor is cooled with cooling water and the second one is cooled with synthesis gas. In a membrane dual-type reactor the wall of the tubes in the gas-cooled reactor is covered with a pd–Ag membrane, which is only hydrogen-permselective. The simulation results have been shown that there are optimum values of reacting gas and coolants temperatures to maximize the overall methanol production. Here, genetic algorithms have been used as powerful methods for optimization of complex problems. In this study, the optimization of the reactor has been investigated in two approaches. In the first approach, the optimal temperature profile along the reactor has been studied and then a stepwise approach has been followed to determine the optimal profiles for saturated water and gas temperatures in three steps during the time of operations to maximize the methanol production rate. The optimization methods have enhanced 5.14% and 5.95% additional yield throughout 4 years of catalyst lifetime for first and second optimization approaches, respectively.