Neuro-genetic aided design of modified H-ZSM-5 catalyst for catalytic conversion of methanol to gasoline range hydrocarbons

The design of modified H-ZSM-5 catalyst for catalytic conversion of methanol to gasoline range hydrocarbons (MTG) was carried out using neuro-genetic approach. Four factors: SiO2/Al2O3 ratio in zeolite (denoted as Si/Al2), calcination temperature (Tc), reaction temperature (Tr) and nominal weight loadings (Lm) of three metals (Ag, Cu, Co) were modeled simultaneously by using combined experimental data and physicochemical descriptors of metals. The obtained optimum model was used as fitness function for hybrid genetic algorithm to find the optimum catalyst. The optimum catalyst was Cu/H-ZSM-5 with Si/Al2 = 40, Tc = 490 °C, Tr = 340 °C, and Lm = 9.24 wt% with methanol conversion of 96.23%, and gasoline yield of 13% that resulted in 4.88% increase in gasoline yield compared to H-ZSM-5 catalyst at the same conditions. The higher experimental yield of gasoline for predicted catalyst along with the fact that this approach can be applied easily for a large number of catalysts, confirms that this approach can be used for design of heterogeneous catalysts. UV–Vis DR spectra confirmed the possibility of ⋯O2−⋯Cu2+⋯O2−⋯Cu2+⋯O2−⋯ chain structures in the channels of zeolite for Cu/HZSM-5 catalyst as active components in MTG reaction.

► Neuro-genetic aided design of heterogeneous catalyst.
► Combined experimental data and physicochemical descriptors used for selection of optimum catalyst.
► Virtual screening of catalysts caused remarkable reduction of experimental work.
► Good agreements were achieved between performances of proposed optimization algorithm and experimentally tested catalysts.
► Using this technique can enhance the chance of finding optimum catalysts in combinatorial heterogeneous catalyst design.