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

Area of Science:

• Biotechnology and Bioengineering
• Environmental Science
• Energy Science

Background:

• Flexible operation of biogas plants is key for grid stabilization with increasing renewable energy integration.
• Anaerobic digestion (AD) processes are susceptible to instabilities from shock loadings, impacting efficiency and reliability.
• Traditional monitoring methods often require time-consuming substrate analyses, limiting real-time process control.

Purpose of the Study:

• To identify early warning indicators for anaerobic digestion process instabilities using gas-phase parameters.
• To evaluate the effectiveness of machine learning classifiers in detecting process disturbances.
• To develop a rapid, cost-effective monitoring approach for load-flexible biogas plants.

Main Methods:

• Simulated load-flexible operation of six laboratory-scale anaerobic filters.
• High-temporal resolution monitoring of biogas composition (CH4, CO2, H2) and volume.
• Application of machine learning classifiers (Support Vector Machine, Random Forest, Multi-Layer Perceptron) for state classification.

Main Results:

• Increased variability in gas quality parameters reliably precedes process disturbances in anaerobic digestion.
• Machine learning models achieved up to 80% accuracy in distinguishing between stable and unstable AD states.
• Gas-phase monitoring proved effective in detecting instabilities without the need for substrate analysis.

Conclusions:

• Gas quality variability serves as a robust early warning indicator for anaerobic digestion instabilities.
• Machine learning-based analysis of gas parameters offers a rapid, cost-effective alternative to conventional monitoring methods.
• This approach supports the stable and flexible operation of biogas plants for enhanced grid integration.