Development of a high performance Cu-based ternary oxide catalyst for oxidative steam reforming of methanol using an artificial neural network

Abstract

Abstract The activity of Cu-based mixed oxide catalysts for oxidative steam reforming of methanol (OSRM) was investigated. In order to find a binary Cu–X mixed oxide catalyst with high methanol conversion, high H2selectivity, and low CO selectivity, an artificial neural network (ANN) was applied to relate the physicochemical properties of additive element X and the catalytic performance of the Cu–X mixed oxide catalyst. Experimental results of 14 Cu–X catalysts were used to train the ANN; the trained ANN predicted that Cu–Ca, Cu–Ce, and Cu–Pr oxides are the best candidates among all Cu–X binary catalysts. The same method using physicochemical properties and ANN was applied to find a good third additive for each binary oxide: (i) For the Cu–Ca system, any additive resulted in inferior catalytic performance. (ii) For the Cu–Ce system, Cu–Ce–Mn showed the best performance. (iii) For the Cu–Pr system, Cu–Pr–Ti was best. In the final step, ANN was also applied to improve the performance of the Cu–Pr–Ti catalyst by optimizing the catalyst composition and the preparation conditions (calcination temperature and total metal salt concentration). For the optimization, an L9 orthogonal array, and a grid search (GS) were applied with the ANNs. The optimum Cu–Pr–Ti catalyst was: Cu / Pr / Ti = 58 / 16 / 26 , calcination temperature = 623 K, total metal salt concentration in preparation step = 1 M. The catalyst showed good performance comparable with the best Cu–Zn-based catalyst ever reported: methanol conversion, H2 selectivity, and residual CO concentration were 96%, 78%, and 510 ppm, respectively.