A multi-scale and multi-objective optimization strategy for catalytic distillation process

This work aims to establishing a multi-scale and multi-objective optimization strategy for the catalytic distillation process. Firstly, the catalyst layer efficiency factor correlation that embraces the influence of the catalyst layer structure was obtained and then coupled with the process simulation to establish a multi-scale model for the catalytic distillation process. On this basis, genetic algorithm was employed to realize the multi-objective optimization. The hydrolysis process of methyl acetate in the reactive dividing wall column was investigated. The equipment parameters and operational conditions of the catalytic distillation column, and the structure parameters of the catalyst layer are optimized simultaneously. The results indicated that the minima of the total annual cost, the gas emission cost and the exergy loss decrease by 19.26%, 23.11% and 67.27%, respectively, by comparing with the counterparts obtained from the single-factor sensitivity analysis. Furthermore, the catalyst cost decreases by 30.88% per year.