Effect of the Genetic Algorithm parameters on the optimisation of heterogeneous catalysts

A study of the effect of Genetic Algorithm (GA) configurations on the performance of heterogeneous catalyst optimisation is reported. The GA optimisation procedure is validated on real case studies. Experimental data to construct the benchmarks were collected by means of High-Throughput Experimentation (HTE) on CO oxidation (COox) and selective CO oxidation (Selox) reactions. For the search space mapping, 189 catalysts were tested for the two reaction conditions at different temperatures, resulting in 1134 test reactions from which two benchmarks were derived. For the algorithm configuration, an in-house-implemented GA platform was used enabling a large variety of operator combinations. Because of the typical limitations in the number of parallel experiments that can be carried out in heterogeneous catalysis, the effects of the population size on the robustness and convergence speed were investigated. From this study, general considerations about the algorithm settings (crossover, selection and mutation) to use for the optimisation of heterogeneous catalysts are addressed.