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

材料导报

2026, Vol. 40

Issue (9): 25030238-12 https://doi.org/10.11896/cldb.25030238

无机非金属及其复合材料

机器学习在生物炭生产及吸附领域中的应用研究进展

杨东东, 李芬^*, 杨莹, 王瑞莹, 邢智超, 韩明洪

哈尔滨理工大学材料科学与化学工程学院,哈尔滨 150000

Research Progress on the Application of Machine Learning in BiocharProduction and Adsorption

YANG Dongdong, LI Fen^*, YANG Ying, WANG Ruiying, XING Zhichao, HAN Minghong

College of Materials Science and Chemical Engineering, Harbin University of Science and Technology, Harbin 150000, China

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摘要随着“双碳”战略的深入推进,生物炭因其独特的碳封存能力和功能性应用,已成为环境科学与材料工程领域的研究热点。机器学习(ML)技术的快速发展为生物炭高效吸附性能的开发提供了创新性研究方法。本文系统综述了ML在生物炭制备工艺优化及吸附性能预测领域的最新研究进展:首先阐释了ML技术的基本算法原理及其在生物炭研究中的适用性特征;其次对ML在生物炭制备过程建模,特别对热解产物分布与生物炭特性预测中的主要影响因素进行了分析;在吸附性能评估方面,深入探讨了ML在预测重金属离子、新兴污染物及气态污染物去除效率中的建模方法与应用成效;最后就ML驱动生物炭成本效益进行了一定的分析。通过对现有文献的分析,本文总结了ML在生物炭研究中的技术优势,指出了在数据质量、模型可解释性及跨尺度预测等方面存在的技术瓶颈,并提出融合多组学数据与物理引导ML的前瞻性研究方向。本文可为环境功能材料设计与污染控制技术的智能化发展提供理论参考。

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关键词: 机器学习生物炭生产污染物吸附预测混合模型

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

收稿日期: 2026-05-10 出版日期: 2026-05-10 发布日期: 2026-05-18

ZTFLH:	TQ127.1
	TP181

基金资助: 国家自然科学基金(22278100)

通讯作者: ^*李芬,博士,哈尔滨理工大学硕士研究生导师、教授。主要研究方向为纳米脱硫剂的制备、脱臭性能分析与应用。hgxylf@126.com

作者简介: 杨东东,哈尔滨理工大学材料科学与化学工程学院硕士研究生,在李芬教授的指导下进行研究。目前主要从事机器学习对生物炭吸附恶臭气体的预测性能研究。

引用本文:

杨东东, 李芬, 杨莹, 王瑞莹, 邢智超, 韩明洪. 机器学习在生物炭生产及吸附领域中的应用研究进展[J]. 材料导报, 2026, 40(9): 25030238-12.
YANG Dongdong, LI Fen, YANG Ying, WANG Ruiying, XING Zhichao, HAN Minghong. Research Progress on the Application of Machine Learning in BiocharProduction and Adsorption. Materials Reports, 2026, 40(9): 25030238-12.

链接本文:

https://www.mater-rep.com/CN/10.11896/cldb.25030238 或 https://www.mater-rep.com/CN/Y2026/V40/I9/25030238

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