Optimization of methanol synthesis reactor using genetic algorithms

This paper presents a study on optimization of methanol synthesis reactor to enhance overall production. A mathematical heterogeneous model of the reactor was used for optimization of reactor performance, both at steady state and dynamic conditions. Here, genetic algorithms were used as powerful methods for optimization of complex problems. Initially, optimal temperature profile along the reactor was studied. Then, a stepwise approach was followed to get an optimal two-stage cooling shell to maximize production rate. These optimization problems were performed through steady-state optimizations with regard to dynamic properties of the process. The optimal reactor with two-stage cooling shell presented higher performances. This optimization approach enhanced a 2.9% additional yield throughout 4 years, as catalyst lifetime. Therefore, we can deduce to redesign methanol synthesis reactor with a two-stage cooling shell reactor based on this study.