Catalyst design for methane oxidative coupling by using artificial neural network and hybrid genetic algorithm

Abstract

A new method for catalyst design was discussed based on artificial neural network, which was developed to simulate the relations between catalyst components and catalytic performance in the previous research. For enhancing efficiency of catalyst design, a new hybrid GA tested by TSP was generated for global optimization to design the ‘optimal’ catalyst. A multi-turn design strategy was described. Based on the previous research, the design method was applied for designing multi-component catalyst for methane oxidative coupling, some better catalysts, in which C 2 hydrocarbon yields were greater than 25% were designed. When reacting on the best catalyst, GHSV was 33313 cm 3 g −1 h −1 , CH 4:O 2 was 3, reaction temperature was 1069 K , methane conversion and C 2 hydrocarbon selectivity were 37.79% and 73.50%, respectively (C 2 hydrocarbon yield was 27.78%), which was higher than that of previous reported catalysts on no diluted gas condition, and showed a better prospect for industrialization of methane oxidative coupling. The research also showed that the new catalyst design method is highly efficient and universal.