Artificial Neural Network Modeling of Thermo-catalytic Methane Decomposition for Hydrogen Production

Thermo-catalytic methane decomposition is a prospective route for producing COx free hydrogen. In this study, Bayesian regularization and Levenberg-Marquardt trained multilayer perceptron neural networks were employed in predictive modeling of hydrogen production by thermo-catalytic methane decomposition. Based on the non-linear relationship between the reaction temperature, weight of the catalysts, time of stream, calcination temperature, calcination time, specific volume, and the hydrogen yield, the various topology was configured for the neural network and tested to determine the artificial neuron that would result in the best model performance. The Levenberg-Marquardt trained neural network displayed the best performance with the model topology of 7–16-1 compared with the Bayesian regularization trained network. The model topology of 7–16-1 represents the input units, hidden neuron, and the output unit. The predicted hydrogen yield by the 7–16-1 configured neural network was in strong agreement with the observed value, evidenced by the coefficient of determination (R2) of 0.953 and mean square error of 0.03. A predicted hydrogen yield of 86.56 vol.% was obtained at the reaction temperature of 700 °C, 0.5 g catalyst weight, calcination temperature of 600 °C, calcination time of 240 min, catalyst specific surface area of 24.1 m2/g, the pore volume of 0.03 cm3/g, and 160 min time on stream which is at proximity with the observed value of 84 vol.%. The sensitivity analysis revealed that all the input parameters have varying levels of importance on the model output. However, the intrinsic properties of the catalysts (specific surface area, and the pore volume) have the most significant influence on the predicted hydrogen yield.