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Machine learning applications for biochar studies: A mini-review.

Wei Wang1, Jo-Shu Chang2, Duu-Jong Lee3

  • 1Department of Chemical Engineering, National Taiwan University, Taipei 106, Taiwan.

Bioresource Technology
|January 6, 2024
PubMed
Summary

Machine learning (ML) can accelerate biochar research for carbon sequestration. This review highlights ML applications in biochar production and use, identifying challenges and future directions for this promising carbon sink technology.

Keywords:
ApplicationBiocharHybrid modelMachine learningPerformance

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Area of Science:

  • Environmental Science
  • Material Science
  • Data Science

Background:

  • Biochar is a key carbon sink for reducing carbon emissions.
  • Current biochar development relies on slow, labor-intensive experimental methods.
  • Machine learning (ML) offers a way to streamline biochar research.

Purpose of the Study:

  • To review ML applications in biochar production, characterization, and utilization.
  • To explain common ML algorithms used in biochar studies.
  • To discuss the prospects and challenges of ML in biochar technology.

Main Methods:

  • Literature review of ML applications in biochar research.
  • Explanation of fundamental ML algorithms.
  • Analysis of current trends and limitations.

Main Results:

  • ML is increasingly applied to biochar production, characterization, and applications.
  • Common ML algorithms are being utilized to analyze biochar data.
  • A significant portion of current ML models are trained on lab-scale data.

Conclusions:

  • ML can significantly accelerate biochar technology development.
  • Hybrid models combining ML with mechanism-based analysis show future promise.
  • Developing ML models using pilot or industrial-scale data is crucial for field applications.