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Modeling of a full-scale biogas plant using a dynamic neural network
Mälardalen University, School of Business, Society and Engineering. (MERO)ORCID iD: 0000-0002-3131-0285
Mälardalen University, School of Business, Society and Engineering. (MERO)ORCID iD: 0000-0002-3485-5440
KTH. (MERO)ORCID iD: 0000-0003-0300-0762
2013 (English)Conference paper, Oral presentation with published abstract (Refereed)
Place, publisher, year, edition, pages
2013.
Keywords [en]
neural network, anaerobic digestion, biogas, model
National Category
Bioprocess Technology Bioenergy Energy Systems
Research subject
Energy- and Environmental Engineering; Biotechnology/Chemical Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-21600OAI: oai:DiVA.org:mdh-21600DiVA, id: diva2:649461
Conference
Sardinia 2013, S. Margherita di Pula, September 30 - October 4
Available from: 2013-09-18 Created: 2013-09-18 Last updated: 2017-09-26Bibliographically approved
In thesis
1. System studies of Anaerobic Co-digestion Processes
Open this publication in new window or tab >>System studies of Anaerobic Co-digestion Processes
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Production of biogas through anaerobic digestion is one pathway to achieving the European Union (EU) goals of reducing greenhouse gas emissions, increasing the share of renewable energy, and improving energy efficiency. In this thesis, two different models (Anaerobic Digestion Model No. 1 and an artificial neural network) are used to simulate a full-scale co-digester in order to evaluate the feasibility of such models. This thesis also includes models of two systems to study the inclusion of microalgae in biogas plants and wastewater treatment plants. One of the studies is a life-cycle assessment in which replacement of the ley crop with microalgae is evaluated. The other study concerns the inclusion of microalgae in case studies of biological treatment in three wastewater treatment plants. Finally, the co-digestion between microalgae and sewage sludge has been simulated to evaluate the effect on biogas and methane yield. The results showed that Anaerobic Digestion Model No.1 and the artificial neural network are suitable for replicating the dynamics of a full-scale co-digestion plant. For the tested period, the artificial neural network showed a better fit for biogas and methane content than the Anaerobic Digestion Model No. 1. Simulations showed that co-digestion with microalgae tended to reduce biomethane production. However, this depended on the species and biodegradability of the microalgae. The results also showed that inclusion of microalgae could decrease carbon dioxide emissions in both types of plants and decrease the energy demand of the studied wastewater treatment plants. The extent of the decrease in the wastewater treatment plants depended on surface volume. In the biogas plant, the inclusion of microalgae led to a lower net energy ratio for the methane compared to when using ley crop silage. Both studies show that microalgae cultivation is best suited for use in summer in the northern climate.

Place, publisher, year, edition, pages
Västerås: Mälardalen University Press, 2017
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 237
National Category
Bioenergy
Research subject
Energy- and Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-36515 (URN)978-91-7485-347-6 (ISBN)
Public defence
2017-11-08, Case, Västerås, 09:15 (English)
Opponent
Supervisors
Available from: 2017-09-27 Created: 2017-09-26 Last updated: 2018-09-27Bibliographically approved

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Nordlander, EvaThorin, EvaYan, Jinyue

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CiteExportLink to record
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Citation style
  • apa
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