Artificial neural network aided design of a multi-component catalyst for the steam reforming of methanol

64-66A neural network based on the feed forward back propagation error has been developed for the design and simulation of the catalytic properties of a multi-component system based on Cu-M-Al2O3 (M=Zn,Cr,Zr) for the steam reforming of methanol. Due to the limited size of the data set, cross validation method has to be used to enhance and also evaluate the prediction ability of the network. The best structural organization has been found to include 4,3 ,6,3 nodes in the input, two hidden layers and the output layer respectively