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Modeling and optimization of biogas production from a waste digester using artificial neural network and genetic algorithm

Author

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  • Abu Qdais, H.
  • Bani Hani, K.
  • Shatnawi, N.

Abstract

Artificial neural networks (ANNs) and genetic algorithms (GA) are considered among the latest tools that are used to solve complicated problems that cannot be solved by conventional solutions. The present study utilizes the ANN and GA as tools for simulating and optimizing of biogas production process from the digester of Russaifah biogas plant in Jordan. Operational data of the plant for a period of 177 days were collected and employed in the analysis. The study considered the effect of digester operational parameters, such as temperature (T), total solids (TS), total volatile solids (TVS), and pH on the biogas yield. A multi-layer ANN model with two hidden layers was trained to simulate the digester operation and to predict the methane production. The performance of the ANN model is verified and demonstrated the effectiveness of the model to predict the methane production accurately with correlation coefficient of 0.87.

Suggested Citation

  • Abu Qdais, H. & Bani Hani, K. & Shatnawi, N., 2010. "Modeling and optimization of biogas production from a waste digester using artificial neural network and genetic algorithm," Resources, Conservation & Recycling, Elsevier, vol. 54(6), pages 359-363.
    • Handle: RePEc:eee:recore:v:54:y:2010:i:6:p:359-363
    DOI: 10.1016/j.resconrec.2009.08.012
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    Citations

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    Cited by:

    1. Abdel daiem, Mahmoud M. & Hatata, Ahmed & Galal, Osama H. & Said, Noha & Ahmed, Dalia, 2021. "Prediction of biogas production from anaerobic co-digestion of waste activated sludge and wheat straw using two-dimensional mathematical models and an artificial neural network," Renewable Energy, Elsevier, vol. 178(C), pages 226-240.
    2. Gołębiewski, Bronisław & Trajer, Jędrzej & Jaros, Małgorzata & Winiczenko, Radosław, 2013. "Modelling of the location of vehicle recycling facilities: A case study in Poland," Resources, Conservation & Recycling, Elsevier, vol. 80(C), pages 10-20.
    3. Kunatsa, Tawanda & Xia, Xiaohua, 2021. "Co-digestion of water hyacinth, municipal solid waste and cow dung: A methane optimised biogas–liquid petroleum gas hybrid system," Applied Energy, Elsevier, vol. 304(C).
    4. Kumar, Pankaj & Kumar, Vinod & Singh, Jogendra & Kumar, Piyush, 2021. "Electrokinetic assisted anaerobic digestion of spent mushroom substrate supplemented with sugar mill wastewater for enhanced biogas production," Renewable Energy, Elsevier, vol. 179(C), pages 418-426.
    5. Zhan, Yuanhang & Zhu, Jun, 2024. "Response surface methodology and artificial neural network-genetic algorithm for modeling and optimization of bioenergy production from biochar-improved anaerobic digestion," Applied Energy, Elsevier, vol. 355(C).
    6. Pomeroy, Brett & Grilc, Miha & Likozar, Blaž, 2022. "Artificial neural networks for bio-based chemical production or biorefining: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    7. Abdel daiem, Mahmoud M. & Hatata, Ahmed & Said, Noha, 2022. "Modeling and optimization of semi-continuous anaerobic co-digestion of activated sludge and wheat straw using Nonlinear Autoregressive Exogenous neural network and seagull algorithm," Energy, Elsevier, vol. 241(C).
    8. Han, Yongming & Du, Zilan & Hu, Xuan & Li, Yeqing & Cai, Di & Fan, Jinzhen & Geng, Zhiqiang, 2023. "Production prediction modeling of food waste anaerobic digestion for resources saving based on SMOTE-LSTM," Applied Energy, Elsevier, vol. 352(C).

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