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Related Experiment Video

Updated: May 10, 2026

Continuously-stirred Anaerobic Digester to Convert Organic Wastes into Biogas: System Setup and Basic Operation
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Machine learning for high solid anaerobic digestion: Performance prediction and optimization.

Prabakaran Ganeshan1, Archishman Bose2, Jintae Lee3

  • 1Department of Environmental Science and Engineering, School of Engineering and Sciences, SRM University-AP, Amaravati, Andhra Pradesh 522240, India.

Bioresource Technology
|April 6, 2024
PubMed
Summary

Machine learning accurately predicts biogas production in high-solid systems. Support Vector Machine achieved 91% accuracy, demonstrating AI

Keywords:
AI for AD Process ControlBiogas productionEnergy and AIML for ADSupervised learning

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Area of Science:

  • Biotechnology and biochemical engineering
  • Artificial Intelligence in environmental science
  • Renewable energy systems

Background:

  • Biogas production via anaerobic digestion (AD) is a complex biological process requiring precise control for optimization.
  • Understanding AD dynamics is crucial for enhancing efficiency and stability in biogas generation.
  • High-solid systems present unique challenges in biogas production modeling and control.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting biogas yield in high-solid anaerobic digestion systems.
  • To identify the most accurate machine learning algorithms for biogas production forecasting.
  • To assess the impact of input variables on model performance and identify critical factors affecting biogas yield.

Main Methods:

  • Development of predictive models using machine learning algorithms: Support Vector Machine (SVM), Extra Trees (ET), Decision Trees (DT), Gaussian Process Regression (GPR), and K-Nearest Neighbors (KNN).
  • Utilized two distinct datasets with varying numbers of input variables (Dataset-1: 10 inputs, Dataset-2: 5 inputs).
  • Statistical analysis to compare model performance and assess the significance of differences between datasets.

Main Results:

  • Support Vector Machine (SVM) demonstrated the highest prediction accuracy, achieving R² values of 91% with Dataset-1 and 87% with Dataset-2.
  • Statistical analysis (p=0.377) indicated no significant difference in accuracy between datasets, suggesting accurate predictions are possible with fewer inputs.
  • Loading rate and retention time were identified as critical factors influencing biogas yield predictions.

Conclusions:

  • Machine learning, particularly SVM, offers a viable approach for accurate biogas yield prediction in high-solid anaerobic digestion.
  • The study highlights the potential of Artificial Intelligence (AI) to optimize and control AD processes, enhancing biogas plant performance.
  • A generic AI-driven model can contribute to improved efficiency and reliability in biogas production systems.