Carbon dioxide reforming of methane over Ni-based catalysts: Modeling the effect of process parameters on greenhouse gasses conversion using supervised machine learning algorithms

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Ayodele B.V.
Alsaffar M.A.
Mustapa S.I.
Kanthasamy R.
Wongsakulphasatch S.
Cheng C.K.
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Elsevier B.V.
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This study aims to model the effect of process parameters on the conversion of carbon dioxide (CO2) and methane (CH4) during reforming reaction over Nickel (Ni) catalysts. Various supervised machine learning algorithms were employed for the model development. To determine the best model, different configurations of the multilayer perceptron (MLP) and nonlinear auto-regressive exogenous (NARX) neural network models and their performances were evaluated. The performance of the various models was tested through their ability to predict the conversion of the CO2 and CH4. The best MLP network configurations of 5�15�2, 5�4�2, and 5�7�2 were obtained for the Levenberg-Marquardt-, the Bayesian Regularization-, and the Scaled conjugate gradient-trained MLP, respectively. While optimized NARX neural network configurations of 5�18�2, 5�13�2, and 5�8�2 were obtained for the Levenberg-Marquardt, Bayesian Regularization, and the Scaled conjugate gradient training algorithms, respectively. The Bayesian Regularization trained NARX with a coefficient of determination (R2) of 0.998 and MSE of 3.24�10�9 displayed the best performance with an accurate prediction of the thermo-catalytic conversion of CH4 and CO2. The sensitivity analysis revealed that the predicted CH4 and CO2 conversion were influenced in the order of reaction temperature > reduction temperature > calcination temperature > time on stream > Ni loading. � 2021 Elsevier Ltd
Catalysts; Conjugate gradient method; Learning algorithms; Methane; Multilayer neural networks; Multilayers; Sensitivity analysis; Supervised learning; Auto-regressive; Bayesian regularization; CH$-4$; Greenhouse gasse; Multilayers perceptrons; Neural-networks; Nonlinear autoregressive exogenous; Performance; Process parameters; Supervised machine learning; Carbon dioxide