Performance analysis and modeling of bio-hydrogen recovery from agro-industrial wastewater

Abstract

Significant volumes of wastewater are routinely generated during agro-industry processing, amounting to millions of tonnes annually. In line with the circular economy concept, there could be a possibility of simultaneously treating the wastewater and recovering bio-energy resources such as bio-hydrogen. This study aimed to model the effect of different process parameters that could influence wastewater treatment and bio-energy recovery from agro-industrial wastewaters. Three agro-industrial wastewaters from dairy, chicken processing, and palm oil mills were investigated. Eight data-driven machine learning algorithms namely linear support vector machine (LSVM), quadratic support vector machine (QSVM), cubic support vector machine (CSVM), fine Gaussian support vector machine (FGSVM), binary neural network (BNN), rotation quadratic Gaussian process regression (RQGPR), exponential quadratic Gaussian process regression (EQGPR) and exponential Gaussian process regression (EGPR) were employed for the modeling process. The datasets obtained from the three agro-industrial processes were employed to train and test the models. The LSVM, QSVM, and CSVM did not show an impressive performance as indicated by the coefficient of determination (R2) < 0.7 for the prediction of hydrogen produced from wastewaters using the three agro-industrial processes. The LSVM, QSVM, and CSVM models were also characterized by high prediction errors. Superior performance was displayed by FGSVM, BNN, RQGPR, EQGPR, and EQGPR models as indicated by the high R2 > 0.9, an indication of better predictability with minimized prediction errors as indicated by the low root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE).

Machine learning in biohydrogen production: a review

“…In keeping with the principles of the circular economy, it may be possible to recover bioenergy resources like biohydrogen from sewage at the same time as it is being treated. Scientists investigated the accuracy of different machine-learning models to recover biohydrogen (Safdar Hossain et al, 2022). A total of eight different datadriven machine learning algorithms were used to create the models, including the cubic support vector machine (CSVM), exponential quadratic Gaussian process regression (EQGPR), fine Gaussian support vector machine (FGSVM), binary neural network (BNN), quadratic gaussian process regression (RQGPR), linear support vector machine (LSVM), rotational quadratic support vector machine (QSVM), and exponential gaussian process regression (EGPR).…”