Optimization of hydrogen production via toluene steam reforming over Ni-Co supported modified-activated carbon using ANN coupled GA and RSM

Hydrogen (H-2) is a clean fuel that can be produced from various resources including biomass. Optimization of H-2 production from catalytic steam reforming of toluene using response surface methodology (RSM) and artificial neural network coupled genetic algorithm (ANN-GA) models has been investigated. In RSM model, the central composite design (CCD) is employed in the experimental design. The CCD conditions are temperature (500-900 degrees C), feed flow rate (0.006-0.034 ml/min), catalyst weight (0.1-0.5 g) and steam-to carbon molar ratio (1-9). ANN model employs a three-layered feed-forward back propagation neural network in conjugation with the tangent sigmoid (tansig) and linear (purelin) as the transfer functions and Levenberg-Marquardt training algorithm. Best network structure of 4-14-1 is developed and utilized in the GA optimization for determining the optimum conditions. An optimum H-2 yield of 92.6% and 81.4% with 1.19% and 6.02% prediction error are obtained from ANN-GA and RSM models, respectively. The predictive capabilities of the two models are evaluated by statistical parameters, including the coefficient of determination (R-2) and root mean square error (RMSE). Higher R-2 and lower RSME values are reported for ANN-GA model (R-2 = 0.95, RMSE = 4.09) demonstrating the superiority of ANN-GA in determining the nonlinear behavior compared to RSM model (R-2 = 0.87, RMSE = 6.92). These results infer that ANN-GA is a more reliable and robust predictive steam reforming modelling tool for H-2 production optimization compared to RSM model. (C) 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

Automated summary

The study investigated the optimization of hydrogen production from catalytic steam reforming of toluene using response surface methodology and artificial neural network coupled genetic algorithm models. The ANN-GA model with a three-layered feed-forward neural network showed higher predictive capabilities compared to the RSM model.