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

[Display omitted] •ANN model was used to simulate for prediction and maximization of ACd.•Bayesian Regularization algorithm showed the best performance.•According to the ANN model, the co-digestion might improve the methane yield up to 183% compared to mono substrates. The production of biofuel from...

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Bibliographic Details
Published in:Fuel (Guildford) Vol. 306; p. 121715
Main Authors: Özarslan, Saliha, Abut, Serdar, Atelge, M.R., Kaya, M., Unalan, S.
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 15.12.2021
Elsevier BV
Subjects:
Algorithms
ANN modeling
ANN simulation
Artificial neural networks
Availability
Batch reactors
Bayesian analysis
Bayesian regularization algorithm
Best management practices
Biodegradability
Biodegradation
Biodiesel fuels
Biofuels
Biogas
Computer simulation
Digestion
Environmental hazards
Environmental management
Industrial wastes
Methane
Neural networks
Regularization
Spent tea waste
Substrates
Synergistic effect
Tea
Tea factory waste
Waste management
Tea factory waste
ANN simulation
ANN modeling
Biogas
Spent tea waste
Bayesian regularization algorithm
ISSN:0016-2361, 1873-7153
Online Access:Get full text
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Summary:[Display omitted] •ANN model was used to simulate for prediction and maximization of ACd.•Bayesian Regularization algorithm showed the best performance.•According to the ANN model, the co-digestion might improve the methane yield up to 183% compared to mono substrates. 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.
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ISSN:0016-2361
1873-7153
DOI:10.1016/j.fuel.2021.121715