Dynamic Simulation and Optimization of a Dual‐Type Methanol Reactor Using Genetic Algorithms

Abstract In this investigation, a dynamic simulation and optimization for an auto‐thermal dual‐type methanol synthesis reactor was developed in the presence of catalyst deactivation. Theoretical investigation was performed in order to evaluate the performance, optimal operating conditions, and enhancement of methanol production in an auto‐thermal dual‐type methanol reactor. The proposed reactor model was used to simulate, optimize, and compare the performance of a dual‐type methanol reactor with a conventional methanol reactor. An auto‐thermal dual‐type methanol reactor is a shell‐and‐tube heat exchanger reactor in which the first reactor is cooled with cooling water and the second one is cooled with synthesis gas. The proposed model was validated against daily process data measured of a methanol plant recorded for a period of 4 years. Good agreement was achieved. The optimization was achieve by use of genetic algorithms in two steps and the results show there is a favorable profile of methanol production rate along the dual‐type reactor relative to the conventional‐type reactor. Initially, the optimal ratio of reactor lengths and temperature profiles along the reactor were obtained. Then, the approach was followed to get an optimal temperature profile at three periods of operation to maximize production rate. These optimization approaches increased by 4.7 % and 5.8 % additional yield, respectively, throughout 4 years, as catalyst lifetime. Therefore, the performance of the methanol reactor system improves using optimized dual‐type methanol reactor.