Optimization of OCM reactions over NaWMn/SiO2 catalyst at elevated pressure using artificial neural network and response surface methodology

In this study, Response Surface Methodology (RSM) and Artificial Neural Network (ANN) predictive models are developed, based on experimental data of the Oxidative Coupling of Methane (OCM) over NaWMn/SiO2 at 0.4 MPa, which was obtained in an isothermal fixed bed reactor. Results show that the simulation and prediction accuracy of ANN was apparently higher compared to RSM. Thus, the Hybrid Genetic Algorithm (HGA), based on developed ANN models, was used for simultaneous maximization of CH4 conversion and C2C selectivity. The pareto optimal solutions show that at a reaction temperature of 987 K, feed GHSV of 15790 h??1, diluents amounts of 20 mole%, and methane to oxygen molar ratio of 3.5, the maximum C2C yield obtained from ANN-HGA was 23.91% (CH4 conversion of 34.6% and C2C selectivity of 69%), as compared to 22.81% from the experimental measurements (CH4 conversion of 34.0% and C2C selectivity of 67.1%). The predicted error in optimum yield by ANN-HGA was 4.81%, suggesting that the combination of ANN models with the hybrid genetic algorithm could be used to find a suitable operating condition for the OCM process at elevated pressures.