机器学习在生物炭生产及吸附领域中的应用研究进展

doi:10.11896/cldb.25030238

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Abstract With the deepening implementation of the "Dual Carbon" strategy (carbon peaking and carbon neutrality), biochar has emerged as a research hotspot in environmental science and materials engineering due to its unique carbon sequestration capacity and functional applications. The rapid advancement of machine learning (ML) technologies has provided innovative methodologies for developing biochar’s high-efficiency adsorption performance. This review systematically examines recent advancements in ML applications for optimizing biochar production processes and predicting adsorption performance. First, elucidates the fundamental principles of ML algorithms and their applicability in biochar research. Next, analyzes ML-driven modeling of biochar production, particularly focusing on key factors influencing pyrolysis product distribution and biochar property prediction. In terms of adsorption performance evaluation, delves into ML-based modeling approaches and their efficacy in predicting the removal efficiency of heavy metal ions, emerging contaminants, and gaseous pollutants. Finally, an analysis is conducted on the cost-effectiveness of machine learning-driven biochar. Through a critical analysis of existing literature, this review highlights the technical advantages of ML in biochar research, identifies persistent challenges such as data quality limitations, model interpretability, and cross-scale prediction uncertainties, and proposes forward-looking research directions integrating multi-omics data with physics-guided ML frameworks. This work aims to provide theoretical insights into the intelligent design of environmental functional materials and the advancement of pollution control technologies.

Key words： machine learning biochar production pollutant adsorption prediction hybrid models

Received: 10 May 2026 Published: Online: 2026-05-18

CLC number:	TQ127.1
	TP181

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	Articles by authors
	YANG Dongdong
	LI Fen
	YANG Ying
	WANG Ruiying
	XING Zhichao
	HAN Minghong

Cite this article:

YANG Dongdong,LI Fen,YANG Ying, et al. Research Progress on the Application of Machine Learning in BiocharProduction and Adsorption[J]. Materials Reports, 2026, 40(9): 25030238-12.

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https://www.mater-rep.com/EN/10.11896/cldb.25030238 OR https://www.mater-rep.com/EN/Y2026/V40/I9/25030238

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