| Summary: | 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 H<sub>2</sub> production by methane dry reforming over a Co/Pr<sub>2</sub>O<sub>3</sub> catalyst. The dataset employed for the ANN modeling was obtained using a central composite experimental design. The input parameters consisted of CH<sub>4</sub> partial pressure, CO<sub>2</sub> partial pressure, and reaction temperature, while the target parameters included the rate of CO and H<sub>2</sub> 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 H<sub>2</sub> 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 (R<sup>2</sup> > 0.9) obtained from the parity plots are an indication that the predicted rates of CO and H<sub>2</sub> production were strongly correlated with the observed values.
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