Optimization and physicochemical studies of alumina supported samarium oxide based catalysts using artificial neural network in methanation reaction

Abstract

Developed countries are increasing their demand for natural gas as it is an industrial requirement for fuel transportation. Most of modern society relies heavily on vehicles. However, the presence of CO2 gas has led to the categorization of sour natural gas which reduces the quality and price of natural gas. Therefore, the catalytic methanation technique was applied to convert carbon dioxide (CO2) to methane (CH4) gas and reduce the emissions of CO2 within the environment. In this study, samarium oxide supported on alumina doped with ruthenium and manganese was synthesized via wet impregnation. X-ray diffraction (XRD) analysis revealed samarium oxide, Sm2O3 and manganese oxide, MnO2 as an active species. The reduction temperature for active species was at a low reaction temperature, 268.2oC with medium basicity site as in Temperature Programme Reduction (TPR) and Temperature Programme Desorption (TPD) analyses. Field Emission Scanning Electron Microscopy (FESEM) analysis showed an agglomeration of particle size. The characterised potential catalyst of Ru/Mn/Sm (5:35:60)/Al2O3 (RMS 5:35:60) calcined at 1,000oC revealed 100% conversion of CO2 with 68.87% CH4 formation at the reaction temperature of 400oC. These results were verified by artificial neural network (ANN) with validation R2 of 0.99 indicating all modelling data are acceptable.