TY - JOUR
T1 - Performance analysis and modeling of bio-hydrogen recovery from agro-industrial wastewater
AU - Safdar Hossain, S. K.
AU - Sadiq Ali, Syed
AU - Cheng, Chin Kui
AU - Ayodele, Bamidele Victor
N1 - Funding Information:
Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia (Project No. Grant 736).
Publisher Copyright:
Copyright © 2022 Safdar Hossain, Sadiq Ali, Cheng and Ayodele.
PY - 2022/9/19
Y1 - 2022/9/19
N2 - 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).
AB - 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).
KW - agro-industrial wastewater
KW - binary neural network
KW - bio-hydrogen
KW - Gaussian process regression
KW - support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85139184301&partnerID=8YFLogxK
U2 - 10.3389/fenrg.2022.980360
DO - 10.3389/fenrg.2022.980360
M3 - Article
AN - SCOPUS:85139184301
SN - 2296-598X
VL - 10
JO - Frontiers in Energy Research
JF - Frontiers in Energy Research
M1 - 980360
ER -