Abstract
This study investigates the applicability of the Leven–Marquardt algorithm, Bayesian regularization, and a scaled conjugate gradient algorithm as training algorithms for an artificial neural network (ANN) predictively modeling the rate of CO and H2 production by methane dry reforming over a Co/Pr2O3 catalyst. The dataset employed for the ANN modeling was obtained using a central composite experimental design. The input parameters consisted of CH4 partial pressure, CO2 partial pressure, and reaction temperature, while the target parameters included the rate of CO and H2 production. A neural network architecture of 3 13 2, 3 15 2, and 3 15 2 representing the input layer, hidden neuron layer, and target (output) layer were employed for the Leven–Marquardt, Bayesian regularization, and scaled conjugate gradient training algorithms, respectively. The ANN training with each of the algorithms resulted in an accurate prediction of the rate of CO and H2 production. The best prediction was, however, obtained using the Bayesian regularization algorithm with the lowest standard error of estimates (SEE). The high values of coefficient of determination (R2 > 0.9) obtained from the parity plots are an indication that the predicted rates of CO and H2 production were strongly correlated with the observed values.
Highlights
Methane dry reforming is a thermo-catalytic process used for producing synthetic gas, a mixture of hydrogen (H2 ) and carbon monoxide (CO), by utilizing methane (CH4 ) and carbon dioxide (CO2 ) as feedstocks [1]
There are several processes such as steam methane reforming [2], coal gasification [3], glycerol reforming [4], and partial oxidation reforming [5] that can be employed for syngas production, none of these processes have the advantages of mitigating greenhouse gas emission through the consumption of CH4 and CO2 [6]
The data consist of 50 experimental runs which are made up of treatment combinations of reaction temperature, CH4 partial pressure, and CO2 partial pressure as input parameters, while the target parameters include the rate of CO and H2 production
Summary
Methane dry reforming is a thermo-catalytic process used for producing synthetic gas (syngas), a mixture of hydrogen (H2 ) and carbon monoxide (CO), by utilizing methane (CH4 ) and carbon dioxide (CO2 ) as feedstocks [1]. One of the key challenges of the methane dry reforming process is catalyst deactivation by carbon deposition and sintering which is caused due to the high temperature (>873 K) required for the reaction [9] To overcome these challenges, several supported metal-based catalysts have been developed and tested. One major challenge is understanding the kinetics of the methane dry reforming in terms of the rate of H2 and CO production due to variations in the chemical composition of the various catalysts [17] This challenge can be overcome by employing an artificial intelligence modeling approach for a better understanding of the process parameters [18,19]. The effectiveness of each of the trained ANN configurations was tested through the predicted rate of H2 and CO production from the Co/Pr2 O3 -catalyzed methane dry reforming process
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