Machine learning applications for biochar studies: A mini-review

Wei Wang, Jo Shu Chang, Duu Jong Lee

Research output: Contribution to journalReview articlepeer-review

5 Citations (Scopus)

Abstract

Biochar is a promising carbon sink whose application can assist in reducing carbon emissions. Development of this technology currently relies on experimental trials, which are time-consuming and labor-intensive. Machine learning (ML) technology presents a potential solution for streamlining this process. This review summarizes the current research on ML's applications in biochar production, characterization, and applications. It briefly explains commonly used machine learning algorithms and discusses prospects and challenges. A hybrid model that combines ML with mechanism-based analysis could be a future trend, addressing the ML's black-box nature. While biochar studies have adopted ML technology, current works mostly use lab-scale data for model training. Further work is needed to develop ML models based on pilot or industrial-scale data to realize the use of ML techniques for the field application of biochar.

Original languageEnglish
Article number130291
JournalBioresource technology
Volume394
DOIs
Publication statusPublished - 2024 Feb

All Science Journal Classification (ASJC) codes

  • Bioengineering
  • Environmental Engineering
  • Renewable Energy, Sustainability and the Environment
  • Waste Management and Disposal

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