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Artificial neural network was applied for the estimation of strength of acid sites synergistically generated on binary mixed oxides. The acid strength of a series of mixed oxides with one component fixed could be estimated within a reasonable error. when two corresponding mixed oxides are included in the training set. It was shown that the neural network can be a powerful tool to reduce the number of experimental runs required to evaluate the synergistic effect over a wide range of possible combinations of components.

DETERMINATION OF SYNERGISTICALLY GENERATED ACID STRENGTH BY NEURAL NETWORK COMBINED WITH EXPERIMENT