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Applying the Neural Network Technologies for Cyclohexane Industrial Oxidation Data Analysis

An algorithm for digital processing of data of chemical reactions of liquid-phase oxidation of substances is developed. Such reactions make it possible to obtain valuable oxygen-containing compounds on an industrial scale. The intelligent scheme of the algorithm is based on a multilayer neural network. Qualitative modeling of the influence of the primary concentration of reagents on the dynamics of the flow and the result of the catalysis reaction is carried out. The simulation results were analyzed using an artificial neural network for various catalysts and reagents, including cyclohexane hydroperoxide, cyclohexanol, cyclohexanone, acids and esters. After training, the neural network with high accuracy reproduces the data from a sample of experiments used during training. It also predicts the results of the study in extended time ranges, as well as with higher values of concentrations of catalytic impurities. Formulated and substantiated predictions allow experimenters to choose the most optimal and promising set of concentrations of active catalytic substances.