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Methane Concentration Prediction in Anaerobic Codigestion Using Multiple Linear Regression with Integrated Microbial

Iván Ostos1, Iván Ruiz1, Diego Cruz2

  • 1Grupo de Investigación en Ingeniería Electrónica, Industrial, Ambiental, Metrología GIEIAM, Universidad Santiago de Cali, Cali 760036, Colombia.

Bioengineering (Basel, Switzerland)
|November 27, 2025
PubMed
Summary

A new model accurately predicts methane concentration from anaerobic codigestion using operational data and microbial profiles. This innovation aids clean energy generation in rural settings.

Keywords:
MLRbiogascodigestionmetagenomics

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Area of Science:

  • Biotechnology and Bioengineering
  • Environmental Science
  • Microbiology

Background:

  • Anaerobic codigestion enhances methane recovery from organic waste.
  • Real-time methane estimation is challenging due to microbial complexity and operational variability, especially in rural areas.

Purpose of the Study:

  • To develop a practical model for predicting methane concentration in anaerobic codigestion systems.
  • To integrate microbial community data for improved prediction accuracy.

Main Methods:

  • Developed a multiple linear regression model using operational data and 16S rRNA gene sequencing for microbial profiles.
  • Utilized a hybrid approach for predictor variable selection, combining statistical correlation and microbial functional relevance.
  • Trained the model on 70% of the data and validated on a 30% test set.

Main Results:

  • The model achieved a coefficient of determination (R²) of 0.92 and a mean relative error (MRE) of 6.50% on the test set.
  • The model effectively integrates microbial community data, capturing biological variations.
  • The prediction system requires minimal computational resources and a limited set of inputs.

Conclusions:

  • The developed model offers a practical and accessible solution for estimating methane levels in decentralized anaerobic digestion systems.
  • Integrating microbial data significantly improves prediction accuracy beyond operational parameters alone.
  • This approach supports local decision-making for clean energy generation and aligns with Sustainable Development Goal 7.