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

Abstract

Hydrogen (H2) is a clean fuel that can be produced from various resources including biomass. Optimization of H2 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 °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 backpropagation 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 H2 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 (R2) and root mean square error (RMSE). Higher R2 and lower RSME values are reported for ANN-GA model (R2 = 0.95, RMSE = 4.09) demonstrating the superiority of ANN-GA in determining the nonlinear behavior compared to RSM model (R2 = 0.87, RMSE = 6.92). These results infer that ANN-GA is a more reliable and robust predictive steam reforming modelling tool for H2 production optimization compared to RSM model.