REGRESSION-BASED OPTIMIZATION OF THE PARTIAL OXIDATION OF METHANA ON Ni-MgO/α-ALUMINA MONOLITH CATALYST IN A REVERSE-FLOW REACTER

Abstract

This study describes the use of statistical optimization techniques on a laboratory scale reverse flow reactor for the catalytic partial oxidation of methane on Ni-MgO/α-alumina monolith catalyst. The effects of initial temperature (Tini), switching time (τ), total flow rate (F), mole fraction of methane (M), and catalyst length on hydrogen yield and methane conversion are investigated using systematic experimental design. In the first experimental phase, the steepest ascent path was established by ridge analysis to determine the stationary point. The optimum operating conditions were determined in the second experimental phase. The following interactions were found to be significant for methane conversion: M*τ, F*τ and M*F. The interaction of F*τ also affected the hydrogen yield. The third-order interaction of F*τ*M was also found to be statistically significant. The optimum methane conversion value of 57.36% could be obtained by setting switching time, total flow rate and a mole fraction of methane of 4.24 minutes, 543ml/min and 0.6043, respectively. The optimum value of hydrogen yield of 36.73% was reached by setting total flow rate, mole fraction of methane and switching time of 540ml/min, 0.5845 and 4.15 minutes, respectively. The analysis of reactor operation showed that optimization of the system required changing more than one factor and that there is a complex interplay of the different experimental factors. The influence of the factors may all be interpreted in terms of heat release and heat loss in the system.