eprintid: 16363
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/63/63
datestamp: 2023-12-19 03:22:53
lastmod: 2023-12-19 03:22:53
status_changed: 2023-12-19 03:06:07
type: article
metadata_visibility: show
creators_name: Safdar Hossain, S.K.
creators_name: Sadiq Ali, S.
creators_name: Cheng, C.K.
creators_name: Ayodele, B.V.
title: Performance analysis and modeling of bio-hydrogen recovery from agro-industrial wastewater
ispublished: pub
keywords: Errors; Forecasting; Gaussian distribution; Gaussian noise (electronic); Learning algorithms; Mean square error; Recovery; Regression analysis; Vectors, Agro-industrial wastewater; Binary neural networks; Bio-energy; Bio-hydrogen; Exponentials; Gaussian process regression; Industrial wastewaters; Linear Support Vector Machines; Quadratic Gaussian; Support vectors machine, Support vector machines
note: cited By 1
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). Copyright © 2022 Safdar Hossain, Sadiq Ali, Cheng and Ayodele.
date: 2022
publisher: Frontiers Media S.A.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139184301&doi=10.3389%2ffenrg.2022.980360&partnerID=40&md5=8906c16ccfd1aab0eba4845a80654175
id_number: 10.3389/fenrg.2022.980360
full_text_status: none
publication: Frontiers in Energy Research
volume: 10
refereed: TRUE
issn: 2296598X
citation:   Safdar Hossain, S.K. and Sadiq Ali, S. and Cheng, C.K. and Ayodele, B.V.  (2022) Performance analysis and modeling of bio-hydrogen recovery from agro-industrial wastewater.  Frontiers in Energy Research, 10.   ISSN 2296598X