The use of neural modelling to estimate the methane production from slurry fermentation processes
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DOI: 10.1016/j.rser.2015.11.093
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- Mellit, A. & Kalogirou, S.A. & Hontoria, L. & Shaari, S., 2009. "Artificial intelligence techniques for sizing photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(2), pages 406-419, February.
- Lönnqvist, Tomas & Silveira, Semida & Sanches-Pereira, Alessandro, 2013. "Swedish resource potential from residues and energy crops to enhance biogas generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 298-314.
- Dach, Jacek & Boniecki, Piotr & Przybył, Jacek & Janczak, Damian & Lewicki, Andrzej & Czekała, Wojciech & Witaszek, Kamil & Rodríguez Carmona, Pablo César & Cieślik, Marta, 2014. "Energetic efficiency analysis of the agricultural biogas plant in 250kWe experimental installation," Energy, Elsevier, vol. 69(C), pages 34-38.
- Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
- Azadeh, A. & Babazadeh, R. & Asadzadeh, S.M., 2013. "Optimum estimation and forecasting of renewable energy consumption by artificial neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 605-612.
- Razavi, M. & Dehghani-sanij, A.R. & Khani, M.R. & Dehghani, M.R., 2015. "Comparing meshless local Petrov–Galerkin and artificial neural networks methods for modeling heat transfer in cisterns," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 521-529.
- Yadav, Amit Kumar & Malik, Hasmat & Chandel, S.S., 2014. "Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 509-519.
- Ganzoury, Mohamed A. & Allam, Nageh K., 2015. "Impact of nanotechnology on biogas production: A mini-review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1392-1404.
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- 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.
- 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).
- 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).
- 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.
- 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.
- 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.
- 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.
- 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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Keywords
Methane emissions; Slurry fermentation; Neural modeling;All these keywords.
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