A soft computing technique applied to industrial catalysis

A Soft Computing technique based on the combination of neural networks and a genetic algorithm has been developed for the discovery and optimization of new catalytic materials when exploring a high-dimensional space. One possible application of this technique is the optimization of the catalytic performance of new solid materials by exploring simultaneously a big number of variables as elemental composition, manufacture procedure variables, etc. Another application is the optimization of process conditions in catalytic reactors at industrial scale. Considering the high temporal and financial costs required for synthesizing and empirically testing potential solid catalysts, the application of Soft Computing techniques in this field seems really interesting, as the number of experiments could be reduced. The proposed system has been validated using two hypothetical functions, based on the modelled behaviour of multi-component catalysts explored in the field of combinatorial catalysis. Moreover, this Soft Computing technique has been applied to an industrial problem, being possible to obtain an optimize Ti-silicate catalyst for the epoxidation of olefins.