RESUMO
Two genetic algorithms for the single- and multiobjective design of combinatorial experiments were applied to the optimization of a solid catalyst system active in the selective catalytic oxidation of propane to propylene. The two different optimization strategies, namely, the single objective optimization of the yield and the multiobjective optimization of the conversion and selectivity were implemented and compared. It was observed that the multiobjective approach optimized the yield in a similar way compared to the single objective approach. With respect to the selectivity, however, the multiobjective outperformed the single objective approach. It was also found that by applying the multiobjective optimization more interesting possible combinations were discovered.
