期刊
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
卷 46, 期 48, 页码 24632-24651出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijhydene.2020.05.033
关键词
Artificial neural network-genetic algorithm (ANN-GA); Response surface methodology (RSM); Steam reforming of toluene; Activated carbon; Nickel-cobalt; Hydrogen
资金
- Universiti Teknologi Malaysia [17H09, 4F988]
- Ministry of Higher Education, Malaysia [17H09, 4F988]
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.
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.
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