Reaction Results of Benzene Alkylation with Propylene Estimation Using the Artificial Neural Network (ANN)

摘要

The usability of artificial neural networks (ANN) for the estimation of the reaction results was investigated. The study was performed by following a model catalysis reaction, benzene alkylation with propylene on zeolite MCM-22. The effects of catalyst characterizations, temperature, the ratio of benzene/propylene (B/P) and weight hourly space velocity (WHSV) on the product distributions were studied. 56 sets of Data obtained from different courses were used for training of the ANN and another 8 sets of data obtained was used for testing of the trained network. This network was designed as a Back-Propagation (BP) network with four neurons in the input layer, "N" neurons in the hidden layer and one neuron in the output layer. The network was trained till the mean square value between the targets and the outputs obtained was 1 ×10 -4 . The conversion of propylene and product distributions for the isopropylbenzene, di-isopropylbenzene and tri-isopropylbenzene were estimated using the trained network. The average relative deviations of determination showed a good correlation between estimated and experimental data sets. There are high correlations between experimental and estimated data curves and that was another proof of the high performance of ANN for estimation of the product distributions of alkylation reaction.