Integration of anaerobic digestion with artificial intelligence to optimise biogas plant operation

Siddharth Swami, Surindra Suthar, Rajesh Singh, Amit Kumar Thakur, Lovi Raj Gupta and Vineet Singh Sikarwar ()
Additional contact information
Siddharth Swami: Doon University
Surindra Suthar: Doon University
Rajesh Singh: Uttaranchal Institute of Technology, Uttaranchal University
Amit Kumar Thakur: Lovely Professional University
Lovi Raj Gupta: Lovely Professional University
Vineet Singh Sikarwar: Institute of Plasma Physics of the Czech Academy of Sciences

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2025, vol. 27, issue 5, No 4, 9773-9803

Abstract: Abstract Energy plays a vital role in executing domestic and industrial activities on a daily basis and therefore, it is fundamental to the development of any nation. Energy obtained from anaerobic digestion (AD) of biodegradable organic biomass is widely used in major economies to fulfill their energy demands and targets. The biogas plant operation is a several stage process, from pre-digestion to post-digestion, and thus requires proper monitoring and evaluation to optimize its operation. Conventional methods of monitoring are insufficient for enhanced output and as a result, numerous researchers around the globe are trying to find a way out for effective monitoring of a biogas plant. As artificial intelligence (AI) is making its way into every aspect of human life and related activities for their enhancement, and its integration with biogas plant operation seems pragmatic. AI technologies could provide a viable solution to the challenges such as process instability, uncertain critical AD parameters, real time monitoring, etc., usually encountered in a biogas plant. The present investigation proposes a comprehensive integration of artificial intelligence technologies with biogas plant operation and biogas production process for better monitoring and evaluation of important AD parameters such as total solid, volatile solid, volatile fatty acid, soluble chemical oxygen demand, pH and temperature. This study highlights the current progress and future AI integration possibilities by proposing an AI conceptual framework through visual representation for biogas plant operation and for process monitoring systems along with different software and hardware components, that possess application in this technological advancement of automation and prediction. Graphical abstract

Keywords: Anaerobic digestion; Artificial intelligence; Automation; Biogas plant; Predictive monitoring; Process optimisation (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10668-023-04326-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:endesu:v:27:y:2025:i:5:d:10.1007_s10668-023-04326-2

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/10668

DOI: 10.1007/s10668-023-04326-2

Access Statistics for this article

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development is currently edited by Luc Hens

More articles in Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().