Simultaneous study of different combinations of ZSM- 5 templates and operating conditions in the MTP process; Designing, Modeling and Optimization by RSM-ANN-GA

Process of converting methanol to propylene is influenced by many parameters. The use of smart techniques can be an effective way to investigate variable parameters and finding optimal conditions. In this work, optimal design of ZSM-5 catalysts with different combinations of templates and operating conditions in methanol to propylene process was performed using response surface methodology and hybrid artificial neural network-genetic algorithm method. Objective functions for optimization were methanol conversion and propylene selectivity. Effects of different variables in the dual-responses system, including molar ratios of tetra propyl ammonium bromide (TPABr), Cetyltrimethylammonium bromide (CTAB), and Pluronic F127, as well as weight hourly space velocity of feed and process temperature on the performance of catalysts, were studied both experimentally and theoretically. Modeling results showed that the designed neural network structure for the process had superior accuracy compared to RSM with correlation coefficients of 0.9976, 0.9950 and 0.9946 for training, validation and testing, respectively. By combining optimal templates, optimum operating temperature of 420 °C and WHSV of 1 h -1 were obtained based on the genetic algorithm to achieve maximum selectivity of propylene and the highest possible conversion of methanol. The optimal catalyst had stable performance under the optimal conditions.