A pseudo-dynamic optimization of a dual-stage methanol synthesis reactor in the face of catalyst deactivation

This paper presents an optimization investigation on methanol synthesis reactor in the face of catalyst deactivation using multi-objective genetic algorithms. Catalyst deactivation is a challenging problem in the operation of methanol synthesis reactor and has an important role on productivity of the reactor. Therefore, determination of the optimal temperature profile along the reactor could be a very important effort in order to cope with catalyst deactivation. Our previous studies clarify the benefits of a two-stage reactor over a single stage reactor. In this study, an optimal temperature trajectory is obtained for each stage of the corresponding two-stage reactor. Here, steady state optimization is performed in six different activity levels by maximizing the yield and minimizing the temperature of the first stage of the reactor. Multi-objective genetic algorithms are used to solve this two-objective optimization. The set of optimal solutions obtained for six activity levels represents an optimal temperature trajectory for each stage, which has been extended and proposed as a dynamic optimization. This optimization resulted in an additional 3.6% yield, during the course of 4-year process.