• Predicting anaerobic digestion...

Treffer: Predicting anaerobic digestion stability in load-flexible operation using gas phase indicators and classification algorithms.

Title:
Predicting anaerobic digestion stability in load-flexible operation using gas phase indicators and classification algorithms.
Authors:
Neubauer L; State Institute of Agricultural Engineering and Bioenergy, University of Hohenheim, Garbenstraße 9, 70599 Stuttgart, Germany. Electronic address: leoni.neubauer@uni-hohenheim.de., Krümpel J; State Institute of Agricultural Engineering and Bioenergy, University of Hohenheim, Garbenstraße 9, 70599 Stuttgart, Germany., Khan MT; State Institute of Agricultural Engineering and Bioenergy, University of Hohenheim, Garbenstraße 9, 70599 Stuttgart, Germany., Lemmer A; State Institute of Agricultural Engineering and Bioenergy, University of Hohenheim, Garbenstraße 9, 70599 Stuttgart, Germany.
Source:
Bioresource technology [Bioresour Technol] 2025 Aug; Vol. 429, pp. 132508. Date of Electronic Publication: 2025 Apr 08.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Elsevier Applied Science Country of Publication: England NLM ID: 9889523 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-2976 (Electronic) Linking ISSN: 09608524 NLM ISO Abbreviation: Bioresour Technol Subsets: MEDLINE
Imprint Name(s):
Original Publication: Barking, Essex, England : New York, N.Y. : Elsevier Applied Science ; Elsevier Science Pub. Co., 1991-
MeSH Terms:
Gases*/analysis , Biofuels*/analysis , Algorithms*, Bioreactors/microbiology ; Methane/analysis ; Anaerobiosis ; Classification Algorithms
Contributed Indexing:
Keywords: Anaerobic digestion; Biogas; Load flexible; Process stability
Substance Nomenclature:
0 (Gases)
0 (Biofuels)
OP0UW79H66 (Methane)
Entry Date(s):
Date Created: 20250410 Date Completed: 20250427 Latest Revision: 20250427
Update Code:
20260130
DOI:
10.1016/j.biortech.2025.132508
PMID:
40209911
Database:
MEDLINE

Weitere Informationen

This study investigates early warning indicators for process instabilities in anaerobic digestion caused by shock-loadings in biogas plants, focussing on gas-phase parameters to avoid substrate analyses. With the increasing use of renewable energy sources, improved energy management is essential. Biogas plants can stabilise power grids when operated flexibly. Six laboratory-scale anaerobic filters with varying organic loading rates were used to simulate load-flexible operation. Gas parameters (CH<subscript>4</subscript>, CO<subscript>2</subscript>, H<subscript>2</subscript>, and volume) were monitored at 40-minute intervals. The analysis showed that gas quality variability can serve as an early warning, with increased variability preceding disturbances. Machine learning classifiers, i.e. Support Vector Machine, Random Forest, and Multi-Layer Perceptron, were used to distinguish between stable and unstable states achieving 80% accuracy. Compared to conventional methods (e.g., volatile fatty acid to alkalinity ratio), these methods offer a cost-effective, rapid approach for monitoring load-flexible biogas plants, providing insights without frequent laboratory analyses and high temporal resolution.
(Copyright © 2025 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.