Neural network modeling the effect of oxygenate additives on the performance of Pt–Sn/γ-Al2O3 catalyst in propane dehydrogenation

Abstract

The effect of oxygenate additives, water and methanol, to the feed on the performance of industrial Pt–Sn/γ-Al2O3 catalyst in dehydrogenation of propane was studied by neural network modeling. Because of the complex nature of the system and very low levels of oxygenate addition, neural networks were employed as an efficient and accurate tool to obtain the behavior of the system. Dehydrogenation reaction was carried out in a fixed-bed quartz reactor in the temperature range of 575–620 °C. Steady state modeling was performed in three different levels of oxygenate addition, and conversion and selectivity at different levels. The optimum amounts of water and methanol for reaction temperatures of 575, 600 and 620 °C were found to be 83.60, 125.40 and 139.34 ppm, respectively, for water and 9.98, 24.94 and 49.88 ppm for methanol by neural network method. The neural network-based optimum was compared with that obtained from experimental data. In this case, various architectures have been checked using 70 % of experimental data for training of artificial neural network (ANN). Among the various architectures multi layer perceptron network with trainlm training algorithm was found as the best architecture. Temperature and water or methanol amount for the present constituents in the feed were network input data. Output data were conversion, selectivity to propylene and yield of propylene. Comparing the obtained ANN model results with 30 % of unseen data confirms ANN excellent estimation performance. The influence of different operating conditions on the accuracy of the results was also investigated and discussed. The propylene yields, however, passed a maximum at the optimum levels of oxygenates coincided with a substantial reduction of coke formation as well. The modeling results were accurate with <0.9 % error.