The use of neural modelling to estimate the methane production from slurry fermentation processes. (April 2016)
- Record Type:
- Journal Article
- Title:
- The use of neural modelling to estimate the methane production from slurry fermentation processes. (April 2016)
- Main Title:
- The use of neural modelling to estimate the methane production from slurry fermentation processes
- Authors:
- Dach, J.
Koszela, K.
Boniecki, P.
Zaborowicz, M.
Lewicki, A.
Czekała, W.
Skwarcz, J.
Qiao, Wei
Piekarska-Boniecka, H.
Białobrzewski, I. - Abstract:
- 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. This study aims to develop a neural model that forecasts the level of methane emission during the slurry fermentation process. This study demonstrates that the generated neural predictor constitutes an efficient tool for estimating the amount of methane produced during bovine and porcine slurry fermentation processes.
- Is Part Of:
- Renewable & sustainable energy reviews. Volume 56(2016:Apr.)
- Journal:
- Renewable & sustainable energy reviews
- Issue:
- Volume 56(2016:Apr.)
- Issue Display:
- Volume 56 (2016)
- Year:
- 2016
- Volume:
- 56
- Issue Sort Value:
- 2016-0056-0000-0000
- Page Start:
- 603
- Page End:
- 610
- Publication Date:
- 2016-04
- Subjects:
- Methane emissions -- Slurry fermentation -- Neural modeling
Renewable energy sources -- Periodicals
Power resources -- Periodicals
Énergies renouvelables -- Périodiques
Ressources énergétiques -- Périodiques
333.794 - Journal URLs:
- http://www.sciencedirect.com/science/journal/13640321 ↗
http://www.elsevier.com/journals ↗
http://www.journals.elsevier.com/renewable-and-sustainable-energy-reviews ↗ - DOI:
- 10.1016/j.rser.2015.11.093 ↗
- Languages:
- English
- ISSNs:
- 1364-0321
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 7364.186000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 8575.xml