화학공학소재연구정보센터
International Journal of Hydrogen Energy, Vol.43, No.15, 7722-7730, 2018
Hydrogen production by a Pd-Ag membrane reactor during glycerol steam reforming: ANN modeling study
In the present work, an artificial neural networks (ANNs) model has been developed for investigation of glycerol steam reforming (GSR) process with Pd-Ag membrane reactor (MR) in the presence of Co/Al2O3 catalyst. Reaction pressure and sweep factor as independent variables (Inputs) and glycerol conversion, hydrogen recovery, hydrogen yield, H-2 selectivity, CO selectivity and CO2 selectivity as dependent variables (outputs) are chosen for ANN modeling of GSR. The ANN model was developed by feed-forward back propagation network with trainlm algorithm and topology (2: 10: 6) and Sigmoid transfer function for hidden and output layers. A good agreement between predicted values using ANN with experimental results was observed (R-2 and MSE values were 0.9998 and 3.48 x 10(-6) (based on normalized data), respectively). Modeling results indicated that all selected factors (reaction pressure and sweep factor) were effective on output variables. It was found that the reaction pressure with a relative importance of 59% was the most effective parameter in the GSR process with Pd-Ag MR in the presence of Co/Al2O3 catalyst. (C) 2017 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.