Optimization of a three-phase slurry catalytic reactor by Particle Swarm

The Particle Swarm Optimization (PSO) technique was employed to optimize an industrial chemical process characterized by being difficult to be optimized by conventional methods. The chemical process is a three phase catalytic slurry reactor in which the reaction of the hydrogenation of o-cresol producing 2-methyl-cyclohexanol is carried out. The process was represented by a multivariable non-linear mathematical model. In order to optimize the process, different PSO algorithms were coupled with the model of the reactor. The aim of the optimization was the searching of the process inputs that maximizes the productivity of 2-methyl-cyclohexanol subject to the constraint of conversion. PSO was able to solve the non-linear constrained optimization problem. Considering the differences among the PSO algorithms, i.e. the velocity equation and parameters values, only the latter showed to have effect on the optimization. The low computational time demanded in the optimization suggests that the PSO can be suitable for real time implementations.