Robust Multi-Objective Optimization for Response Surface Models Applied to Direct Low-Value Natural Gas Conversion...

The high proportion of CO₂/CH₄ in low aggregated value natural gas compositions can be used strategically and intelligently to produce more hydrocarbons through oxidative methane coupling (OCM). The main goal of this study was to optimize direct low-value natural gas conversion via CO₂-OCM on metal oxide catalysts using robust multi-objective optimization based on an entropic measure to choose the most preferred Pareto optimal point as the problem's final solution. The responses of CH₄ conversion, C₂ selectivity, and C₂ yield are modeled using the response surface methodology. In this methodology, decision variables, e.g., the CO₂/CH₄ ratio, reactor temperature, wt.% CaO and wt.% MnO in ceria catalyst, are all employed. The Pareto optimal solution was obtained via the following combination of process parameters: CO₂/CH₄ ratio = 2.50, reactor temperature = 1179.5 K, wt.% CaO in ceria catalyst = 17.2%, wt.% MnO in ceria catalyst = 6.0%. By using the optimal weighting strategy w₁ = 0.2602, w₂ = 0.3203, w₃ = 0.4295, the simultaneous optimal values for the objective functions were: CH₄ conversion = 8.806%, C₂ selectivity = 51.468%, C₂ yield = 3.275%. Finally, an entropic measure used as a decision-making criterion was found to be useful in mapping the regions of minimal variation among the Pareto optimal responses and the results obtained, and this demonstrates that the optimization weights exert influence on the forecast variation of the obtained response.