Coatings of Na-aluminosilicate zeolites prepared using predictions from an artificial neural network method

Abstract

Predictions made by the Generalized Regression Neural Networks (GRNN) method were used to relate the initial compositions of various reaction mixtures to the types of Na-aluminosilicate zeolites that may be obtained from these compositions. In the light of the predictions made, coatings were prepared on stainless steel plates, which were characterized by X-ray diffraction and scanning electron microscopy prior to and after syntheses. Coatings of zeolites P, X, A, analcime as well as their mixtures could be obtained from a variety of previously unknown clear solution compositions, generally in good accordance with the predictions made by the GRNN method. Different textural properties were obtained for the coatings of the same zeolite, such as P and X, which could be prepared from a relatively wide range of compositions.