Artificial neural network methods for the estimation of zeolite molar compositions that form from different reaction mixtures

The possibility of using artificial neural network (ANNs) methods for the estimation of the zeolite molar composition and hence the zeolite phase that may be obtained from a certain initial reaction mixture composition was investigated. Three different artificial neural network methods, namely feed forward back propagation (FFBP), radial basis function-based neural networks (RBF) and generalized regression neural networks (GRNN), were tested for this purpose. A data set obtained from the literature was used in the training of the neural networks. The results obtained for a second data set were compared to experimental findings as well as to estimations made by using multilinear and non-linear regression. It was determined that the neural networks learn quite efficiently from experimental zeolite synthesis data. The predictions made by using artificial neural network methods were, in general, more reliable than those performed by regression. The best prediction of the Si contents of the zeolites investigated were made by the GRNN and FFBP methods while the H2O content was predicted better by the RBF method. The results indicate that using artificial neural network methods may decrease significantly the number of experiments that have to be performed to discover new synthesis compositions.