Application of response surface methodology for the optimization of light olefins production from CO hydrogenation using an efficient catalyst

Abstract

In this paper, statistical analysis of the collective effects of operating variables was applied to optimize the light olefins production conditions from CO hydrogenation in a fixed bed reactor system, by response surface methodology (RSM) for modeling and optimizing the performance of a multivariable FTO synthesis using bimetallic Co-Mn/graphene catalyst. The levels of the independent variables were: temperature (260–320 °C), total pressure (1–5 bar), and CO/H2 ration (1–2). The catalyst was synthesized by an incipient wetness impregnation method, and its physiochemical characterization was performed by using BET, TPR, XRD, FESEM, RAMAN, and TGA techniques. Through this work an 18 experimental design was employed with 4 center run. The analysis of variance (ANOVA) at 95% confidence interval was done and reduced models were produced for the anticipation of the responses. The results of the optimization implies that higher levels of temperature with shorter pressure favor higher selectivity of light olefins fraction. The Fischer-Tropsch to olefins synthesis with the graphene based bimetallic catalysts at high temperatures (>280 °C) and low pressure enhanced the conversion of CO, formation of CH4 and CO2. The reaction temperature was the most significant independent variable on the amount of light olefins, methane and carbon dioxide content. Also, higher pressure (>3.5 bar) and H2/CO (>1) increases the formation of C5+. The optimum values temperature, pressure, and (H2/CO) molar ratio were turned out be 320 °C, 2 bar, and H2/CO = 1, respectively. At these optimum conditions, CO conversion and olefins selectivity were found as 50.25% and 29.55%, respectively.