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Explainable machine learning deciphers and quantifies material-microbe-methane pathways in zero-valent iron-enhanced

Zeyu Ou1, Hui Xu2, Yanyang Zhang2

  • 1State Key Laboratory of Water Pollution Control and Green Resource Recycling, School of Environment, Nanjing University, Nanjing, 210023, China.

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|July 6, 2026
PubMed
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Zero-valent iron (ZVI) enhances anaerobic digestion (AD) methane recovery by altering microbial communities. Machine learning reveals ZVI properties and operations transmit effects through microbes, guiding material selection for optimal methane output.

Keywords:
Anaerobic digestionInterpretable machine learningMethane yield predictionMicrobial mediationZero-valent iron

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Area of Science:

  • Environmental Microbiology
  • Biotechnology
  • Machine Learning Applications

Background:

  • Zero-valent iron (ZVI) is used to boost methane production in anaerobic digestion (AD).
  • The mechanisms linking ZVI properties, microbial community shifts, and methane yield are not fully understood.
  • Optimizing ZVI application across different substrates is challenging due to poorly quantified effects.

Purpose of the Study:

  • To quantify how ZVI material properties and operating conditions influence methane yield through microbial communities in AD.
  • To develop a predictive model for ZVI-enhanced AD performance based on material, operational, and microbial factors.
  • To elucidate the transmission pathways of ZVI effects on methane production.

Main Methods:

  • Construction of a cross-study database integrating ZVI descriptors, operating parameters, microbial community data, and methane yield.
  • Development and application of a pathway-aware machine learning framework (XGBoost) to analyze effect transmission.
  • Mediation and pathway-decomposition analyses to identify key microbial drivers and transmission mechanisms.

Main Results:

  • The XGBoost model achieved high accuracy (R²=0.916 test, R²=0.817 validation).
  • Key microbial drivers identified include specific methanogens and bacterial phyla (Synergistetes, Proteobacteria).
  • Approximately 72% of ZVI effects on methane yield are mediated by microbial groups, with specific surface area and initial COD being critical factors.

Conclusions:

  • ZVI optimization in AD is substrate-dependent, with material properties (e.g., nano-scale vs. sub-millimeter) needing tailored selection.
  • A predictive model allows scenario forecasting using controllable inputs, even without sequencing data.
  • This study provides a mechanism-guided approach for ZVI selection and process regulation in engineered ecosystems.