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

Özarslan, Saliha; Abut, Serdar; ATELGE, Muhammed Raşit; Kaya, Mustafa; Ünalan, Sebahattin

doi:10.1016/j.fuel.2021.121715

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

Yazarlar (5)
Saliha Özarslan
Serdar Abut Siirt Üniversitesi, Türkiye
Doç. Dr. Muhammed Raşit ATELGE Kurum Bilgileri Mühendislik ve Mimarlık Fakültesi Makine Mühendisliği Otomotiv Özgeçmiş Sayfası İletişim Bilgileri: Siirt Üniversitesi, Türkiye
Mustafa Kaya Siirt Üniversitesi, Türkiye
Sebahattin Ünalan Türkiye

Devamını Göster

Özet

The 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 co-digestion.

Anahtar Kelimeler

Makale Türü	Özgün Makale
Makale Alt Türü	SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale
Dergi Adı	Fuel
Dergi ISSN	0016-2361
Dergi Tarandığı Indeksler	SCI-Expanded
Dergi Grubu	Q1
Makale Dili	İngilizce
Basım Tarihi	12-2021
Cilt No	306
Sayı	121715
Sayfalar	1 / 11
Doi Numarası	10.1016/j.fuel.2021.121715
Makale Linki	http://dx.doi.org/10.1016/j.fuel.2021.121715

BM Sürdürülebilir Kalkınma Amaçları

Atıf Sayıları
SCOPUS	22

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

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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

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