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The use of neural modelling to estimate the methane production from slurry fermentation processes

Author

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  • Dach, J.
  • Koszela, K.
  • Boniecki, P.
  • Zaborowicz, M.
  • Lewicki, A.
  • Czekała, W.
  • Skwarcz, J.
  • Qiao, Wei
  • Piekarska-Boniecka, H.
  • Białobrzewski, I.

Abstract

Slurry constitutes an important substrate, increasingly often forming part of biogas production in biogas plants due to the significant content of methane in biogas produced from slurry. Slurry fermentation leads also to its deodorisation and significantly affects the sanitation process. Biogas production constitutes a microbiological process, one affected by many parameters, both physical and chemical. The complexity of the processes occurring during slurry fermentation means it is difficult to identify the significant parameters of a process. Therefore, the fermentation model is often defined as a “black box” method. Artificial neural networks (ANN) are becoming more frequently recognised as a tool to analyse processes that do not have a formal mathematical description (e.g. in the form of a structural model). Neural models enable one to conduct a comprehensive analysis of an issue, including in the context of forecasting biogas emissions during the slurry fermentation process.

Suggested Citation

  • Dach, J. & Koszela, K. & Boniecki, P. & Zaborowicz, M. & Lewicki, A. & Czekała, W. & Skwarcz, J. & Qiao, Wei & Piekarska-Boniecka, H. & Białobrzewski, I., 2016. "The use of neural modelling to estimate the methane production from slurry fermentation processes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 603-610.
  • Handle: RePEc:eee:rensus:v:56:y:2016:i:c:p:603-610
    DOI: 10.1016/j.rser.2015.11.093
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    Cited by:

    1. 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).
    2. Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Syed Muhammad Arafat & Sher Afghan & Ahmad Hassan Kamal & Muhammad Asim & Muhammad Haider Khan & Muhammad Waqas Rafique & Uwe Naumann & Sajawal Gul Niazi &, 2020. "Optimization of a 660 MW e Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency," Energies, MDPI, vol. 13(21), pages 1-33, October.
    3. Piotr Boniecki & Małgorzata Idzior-Haufa & Agnieszka A. Pilarska & Krzysztof Pilarski & Alicja Kolasa-Wiecek, 2019. "Neural Classification of Compost Maturity by Means of the Self-Organising Feature Map Artificial Neural Network and Learning Vector Quantization Algorithm," IJERPH, MDPI, vol. 16(18), pages 1-9, September.
    4. Sakiewicz, P. & Piotrowski, K. & Ober, J. & Karwot, J., 2020. "Innovative artificial neural network approach for integrated biogas – wastewater treatment system modelling: Effect of plant operating parameters on process intensification," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    5. Kowalczyk-Juśko, Alina & Pochwatka, Patrycja & Zaborowicz, Maciej & Czekała, Wojciech & Mazurkiewicz, Jakub & Mazur, Andrzej & Janczak, Damian & Marczuk, Andrzej & Dach, Jacek, 2020. "Energy value estimation of silages for substrate in biogas plants using an artificial neural network," Energy, Elsevier, vol. 202(C).
    6. Bugała, A. & Zaborowicz, M. & Boniecki, P. & Janczak, D. & Koszela, K. & Czekała, W. & Lewicki, A., 2018. "Short-term forecast of generation of electric energy in photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 306-312.
    7. Susanne Theuerl & Christiane Herrmann & Monika Heiermann & Philipp Grundmann & Niels Landwehr & Ulrich Kreidenweis & Annette Prochnow, 2019. "The Future Agricultural Biogas Plant in Germany: A Vision," Energies, MDPI, vol. 12(3), pages 1-32, January.
    8. Jakub Frankowski & Maciej Zaborowicz & Jacek Dach & Wojciech Czekała & Jacek Przybył, 2020. "Biological Waste Management in the Case of a Pandemic Emergency and Other Natural Disasters. Determination of Bioenergy Production from Floricultural Waste and Modeling of Methane Production Using Dee," Energies, MDPI, vol. 13(11), pages 1-15, June.
    9. Czekała, Wojciech & Bartnikowska, Sylwia & Dach, Jacek & Janczak, Damian & Smurzyńska, Anna & Kozłowski, Kamil & Bugała, Artur & Lewicki, Andrzej & Cieślik, Marta & Typańska, Dorota & Mazurkiewicz, Ja, 2018. "The energy value and economic efficiency of solid biofuels produced from digestate and sawdust," Energy, Elsevier, vol. 159(C), pages 1118-1122.
    10. Wojciech Czekała, 2021. "Solid Fraction of Digestate from Biogas Plant as a Material for Pellets Production," Energies, MDPI, vol. 14(16), pages 1-8, August.

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