Artificial neural network modeling of hydrogen-rich syngas production from methane dry reforming over novel Ni/CaFe2O4 catalysts

In this study, the application of artificial neural networks (ANN) for the modeling of hydrogen-rich syngas produced from methane dry reforming over Ni/CaFe2O4 catalysts was investigated. Multi-layer perceptron (MLP) and radial basis function (RBF) neural network architectures were employed for the modeling of the experimental data obtained from methane dry reforming over novel Ni/CaFe2O3 catalysts. The Ni/CaFe2O3 catalysts were synthesized and characterized by XRD, SEM, EDX and FTIR. The as-synthesized Ni/CaFe2O3 catalysts were tested in a continuous flow fixed bed stainless steel reactor for the production of hydrogen-rich syngas via methane dry reforming. The inputs to the ANN-MLP and ANN-RBF-based models were the catalyst metal loadings (5-15wt %), feed ratio (0.4-1.0) and the reaction temperature (700-800 degrees C). The two models were statistically discriminated in order to measure their predictive capability for the hydrogen-rich syngas production. Coefficient of determination (R-2) values of 0.9726, 0.8597, 0.9638 and 0.9394 obtained from the prediction of H-2 yield, CO yield, CH4 conversion and CO2 conversion respectively using ANN-MLP-based model were higher compared to R-2 values of 0.9218, 0.7759, 0.8307 and 0.7425 obtained for the prediction of H-2 yield, CO yield, CH4 conversion and CO conversion respectively using ANN-RBF-based model. The statistical results showed that the ANN-MLP-based model performed better than ANN-RBF model for the prediction of hydrogen-rich syngas from methane dry reforming over the Ni/CaFe2O4 catalysts. Further t-test performed based on the target outputs from the ANN-MLP and ANN-RBF network shows that the models were statistically significant. (c) 2016 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.