Modeling and simulation of co-digestion performance with artificial neural network for prediction of methane production from tea factory waste with co-substrate of spent tea waste

Basit Öğe Kaydı
dc.authoridAtelge, Muhamed Rasit/0000-0002-0613-2501
dc.contributor.authorOzarslan, Saliha
dc.contributor.authorAbut, Serdar
dc.contributor.authorAtelge, M. R.
dc.contributor.authorKaya, M.
dc.contributor.authorUnalan, S.
dc.date.accessioned2024-12-24T19:27:06Z
dc.date.available2024-12-24T19:27:06Z
dc.date.issued2021
dc.departmentSiirt Üniversitesi
dc.description.abstractThe production of biofuel from waste has become an important topic for waste management and reducing its environmental hazard. Tea factory waste is a strong candidate due to its availability and sourceability. This study aimed to reveal the biochemical methane potential (BMP) of tea factory waste (TFW) and spent tea waste (STW). Additionally, the results revealed that both substrates had high biodegradability due to high VS removal. The BMP tests took 49 days under mesophilic conditions with a batch reactor and the cumulative methane yields were 249 +/- 3, and 261 +/- 8 mL CH4/g VS for TFW and STW, respectively. According to prediction data with the selected ANN model, which was 50 hidden layer sizes, trained with Bayesian Regularization algorithm, the maximum cumulative specific methane yield of the co-digestion was simulated as 468.43 mL CH4/g VS when the ratio of 65 and 35% (w/w by VS) of TFW and STW, respectively. The predicted methane yield for co-substrates was 183% higher than mono substrates. This result revealed that TFW can be a good candidate for biogas production as biofuel for not only its availability and sourceability but also the synergistic effect possible for codigestion.
dc.description.sponsorshipErciyes University [FDK-2020-10493]
dc.description.sponsorshipThe authors would like to acknowledge the Erciyes University for their financial support under FDK-2020-10493. Mr. David Krisa's help in proofreading is highly appreciated by all the authors.
dc.identifier.doi10.1016/j.fuel.2021.121715
dc.identifier.issn0016-2361
dc.identifier.issn1873-7153
dc.identifier.scopus2-s2.0-85113610755
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.fuel.2021.121715
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6505
dc.identifier.volume306
dc.identifier.wosWOS:000703785900001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofFuel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectANN modeling
dc.subjectANN simulation
dc.subjectBayesian regularization algorithm
dc.subjectBiogas
dc.subjectTea factory waste
dc.subjectSpent tea waste
dc.titleModeling and simulation of co-digestion performance with artificial neural network for prediction of methane production from tea factory waste with co-substrate of spent tea waste
dc.typeArticle

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