Bio-hydrogen production from the photocatalytic conversion of wastewater: Parametric analysis and data-driven modelling using nonlinear autoregressive with exogeneous input and back-propagated multilayer perceptron neural networks

• Published in: Fuel (Guildford) 2023-07, Vol.344, p.128026, Article 128026
• Main Authors: Kanthasamy, Ramesh, Ali, Imtiaz, Ayodele, Bamidele Victor, Maddah, Hisham A.
• Format: Article
• Language: English
• Subjects: Biohydrogen; Multilayer perceptron neural networks; Nonlinear autoregressive; Photocatalytic conversion
• ISSN: 0016-2361; 1873-7153
• DOI: 10.1016/j.fuel.2023.128026

•Data-driven modeling of biohydrogen yield from photocatalytic conversion of wastewater.•Application of multilayer perceptron neural network (MLPNN) and nonlinear autoregressive neural network (NARX)•Trained model using Levenberg-Marquardt (LM), Bayesian regularization (BR) and scaled conjugate gradient (SCG) algorithms.•MLPNN-BR model outperformed with R2 of 0.999 and RMSE of 0.138. The quest for energy and environmental sustainability necessitates an increasing interest in the photocatalytic conversion of wastewater to biohydrogen. However, the complexity of the photocatalytic conversion and the low productivity of the biohydrogen produced has become a major concern in the scale-up of the process. This study employs a data-driven approach to model biohydrogen production from the photocatalytic conversion of wastewater. Having ascertained the influence of five different parameters namely catalyst size, reaction temperature, catalyst among, irradiation time, and radiation intensity on the biohydrogen production through parametric analysis, the data were employed to model the process using multilayer perceptron neural network (MLPNN) and nonlinear autoregressive neural network (NARX). Both the MLPNN and NARX models were trained using Levenberg-Marquardt (LM), Bayesian regularization (BR), and scaled conjugate gradient (SCG) algorithms. The performance of 20 network architectures was tested for MLPNN-LM, MLPNN-BR, MLPNN-SCG, NARX-LM, NARX-BR, and NARX-SCG. The analysis revealed that the best network architectures of 5-14-1, 5-11-1, 5-7-1, 5-14-1, 5-15-1, and 5-7-1 were obtained for the MLPNN-LM, MLPNN-BR, MLPNN-SCG, NARX-LM, NARX-BR, and NARX-SCG, respectively. All the models demonstrated a good predictability of the biohydrogen production as evidenced by the coefficient of determination (R2) > 0.9 and low root mean square error (RMSE) values. The best performance was displayed by MLPNN-BR model with R2 of 0.999 and RMSE of 0.138. The independent variable analysis shows that all the factors significantly influence the predicted biohydrogen production. The catalyst size has the most significant effect on the predicted hydrogen production as indicated by the importance value of 0.329.