Analysis of factors controlling catalytic activity by neural network

An artificial neural network was applied to the analysis of factors controlling catalytic activity by taking, as examples, experimentally established correlations of catalytic activities with primary factors including both monotonous and volcano-type correlations. Three equations were proposed and applied to the estimation of relative importance of each given factor from the weightings of connecting links in the trained artificial neural network of an error back-propagation model. In all the examples, the primary factors that had been proposed in experimental studies were successfully identified by using an equation based on our previous proposal. Further, the possibility of identifying secondary factor was also discussed.

[1]  Y. Moro-oka,et al.  Regularities in catalytic properties of metal oxides in propylene oxidation , 1966 .

[2]  P. Pomonis.  The activity patterns of the rare earth oxides in heterogeneous catalysis , 1982 .

[3]  Martin Holeňa,et al.  Feedforward neural networks in catalysis: A tool for the approximation of the dependency of yield on catalyst composition, and for knowledge extraction , 2003 .

[4]  Xiaoqun Wu,et al.  Artificial neural network aided design of catalyst for propane ammoxidation , 1997 .

[5]  G. Ball,et al.  Identifying factors which modify the effects of ambient ozone on white clover (Trifolium repens) in Europe , 1998 .

[6]  Tadashi Hattori,et al.  Neural network as a tool for catalyst development , 1995 .

[7]  T. Hattori,et al.  Catalytic activity of lanthanide oxides in oxidation of butane , 1976 .

[8]  Kohji Omata,et al.  Prediction of effective additives to a Ni/active carbon catalyst for vapor-phase carbonylation of methanol by an artificial neural network , 2004 .

[9]  B. Delmon,et al.  Rationalization of the catalytic behavior of lanthanide oxides and praseodymium molybdates in total and selective oxidation of isobutene , 2001 .

[10]  Tadashi Hattori,et al.  Estimation of the acid strength of mixed oxides by a neural network , 1992 .

[11]  T. Hattori,et al.  DETERMINATION OF SYNERGISTICALLY GENERATED ACID STRENGTH BY NEURAL NETWORK COMBINED WITH EXPERIMENT , 1991 .

[12]  Tadashi Hattori,et al.  Estimation of catalytic performance by neural network — product distribution in oxidative dehydrogenation of ethylbenzene , 1994 .

[13]  Kohji Omata,et al.  Optimization of Cu oxide catalysts for methanol synthesis by combinatorial tools using 96 well microplates, artificial neural network and genetic algorithm , 2004 .