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چکیده
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To enhance the propylene selectivity in catalytic conversion of methanol to propylene (MTP), the bimetallic catalysts were prepared by Mn/H-ZSM-5 with second metal of Ce, Cr, Fe and Ni. In order to design the bimetallic catalysts (M-Mn/H-ZSM-5; M: Ce, Cr, Fe and Ni) and to optimize the propylene selectivity, an artificial neural network (ANN) model was linked with genetic algorithm (GA). Investigation of the optimal catalyst preparation conditions (wt. % of second metal loading, calcination temperature and calcination time) and the atomic descriptors of second metal (electronegativity, melting enthalpy, atomic weight and ionization energy) were carried out by the ANN-GA model simultaneously. The model predicted that the maximum propylene selectivity was produced via Ce-Mn/H-ZSM-5 with the following catalyst preparation conditions: 2.46 wt. % of Ce loading, calcination temperature of 486 °C and calcination time of 4 h. The optimized propylene selectivity of model prediction and the experimental value were 54.3% and 54.8% respectively. The catalyst samples were characterized by XRD, FE-SEM, FT-IR, N2 adsorption/desorption, NH3-TPD and ICP-AES.
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