Modeling photocatalytic hydrogen production from ethanol over copper oxide nanoparticles: a comparative analysis of various machine learning techniques

The production of hydrogen is one way to meet the ever-increasing demand for cleaner and renewable energy sources. In this study, various machine learning techniques such as Levenberg-Marquardt neural networks (LMNN), scaled conjugate gradient descent neural networks (SCDNN), and support vector machine (SVM) in comparison with nonlinear regression model (NLM) and response surface model (RSM) were employed for modeling photocatalytic hydrogen production from ethanol using copper oxide (CuO) nanoparticles as photocatalyst. The effects of input parameters such as the irradiation time, the CuO content, the catalyst dosage, and the ethanol concentration on hydrogen production were considered in the modeling process. Optimized network configurations of 4-12-1 and 4-5-1 representing the input nodes, hidden neurons, and output node were used for the LMNN and SCDNN, respectively. Both the LMNN and SCDNN show superior prediction of hydrogen production compared with the SCDNN, SVM, NLM, and RSM as indicated by the high R values of 0.998 and 0.997 for LMNN and SCDNN, respectively. The LMNN displayed the best prediction of hydrogen production with R value of 0.998. The sensitivity analysis shows that all the input parameters influenced the LMNN model output. However, the predicted hydrogen from the LMNN model was best influenced by the irradiation time.

Automated summary

The study utilized various machine learning techniques to model the photocatalytic hydrogen production from ethanol using CuO nanoparticles as catalyst. The LMNN and SCDNN showed superior prediction performance with high R values of 0.998 and 0.997, respectively. The LMNN displayed the best prediction of hydrogen production. The sensitivity analysis indicated that all input parameters influenced the output of the LMNN model, with the irradiation time having the strongest effect on hydrogen prediction.