基于机器学习的生物炭吸附重金属建模研究进展

doi:10.19965/j.cnki.iwt.2024-0004

摘要/Abstract

摘要：

生物炭吸附重金属试验面临参数众多、污染状况复杂、生物炭特性丰富以及研究成本高且周期长等问题，传统吸附模型已无法满足当前研究需求。近年来机器学习在高维数据处理和复杂问题分析方面展现出巨大潜力，其建模流程清晰且成熟，在重金属污染预测方面精准而稳定，对发掘隐藏的吸附机制具有独特价值，是生物炭吸附重金属建模研究的优质选择。阐述了机器学习建模的工作流程及优势；从吸附效率预测、促进优化实验、洞察吸附机理三方面综述了机器学习在生物炭吸附重金属中的应用；分析了机器学习在生物炭吸附重金属研究领域面临的挑战，并对跨学科合作的前景与发展趋势进行了展望，如构建更加全面可靠的吸附数据库、引入表面官能团等深层影响因素、关注模型准确性和计算成本平衡等以深化对生物炭吸附重金属建模的研究。

关键词: 生物炭, 机器学习, 重金属, 预测模型, 环境修复

Abstract:

Traditional adsorption models are faced with challenges due to various parameters,complex pollution conditions, diverse biochar properties, and the high cost and lengthy duration of research. Machine learning has demonstrated significant potential in handling high-dimensional data and analyzing complex problems. Its clear and mature modeling process provides precise and stable predictions for heavy metal pollution and holds unique value in uncovering hidden adsorption mechanisms, making it an excellent choice for studying heavy metal adsorption by biochar. This paper described the workflow and advantages of machine learning modeling. The application of machine learning in heavy metal adsorption across three aspects of predicting adsorption efficiency, aiding in optimizing experiments, and gaining insights into adsorption mechanisms was summarized. Furthermore, the challenges that machine learning faced in the field of biochar-mediated heavy metal adsorption and interdisciplinary collaboration anticipation were analyzed. Strategies such as establishing a more comprehensive and reliable adsorption database, incorporating surface functional groups as influential factors, emphasizing model accuracy, and balancing computational costs were proposed to deepen the research on modeling heavy metal adsorption by biochar.

Key words: biochar, machine learning, heavy metals, prediction model, environmental remediation

中图分类号:

X703
TP181

冯丁, 刘晶静, 马文丹, 刘玉学, 杨尘, 张萌萌. 基于机器学习的生物炭吸附重金属建模研究进展[J]. 工业水处理, 2024, 44(12): 1-11.

Ding FENG, Jingjing LIU, Wendan MA, Yuxue LIU, Chen YANG, Mengmeng ZHANG. Research progress on modeling of heavy metal adsorption by biochar based on machine learning[J]. Industrial Water Treatment, 2024, 44(12): 1-11.

https://www.iwt.cn/CN/Y2024/V44/I12/1

图/表 4

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机器学习算法	生物炭种类	金属离子	模型最优精度		参考文献
机器学习算法	生物炭种类	金属离子	R ²	RMSE/NRMSE/MSE	参考文献
MRA	香蕉芯生物炭	As（Ⅳ）	0.820 0		〔59〕
ANN、RF	24种生物炭	Pb（Ⅱ）等6种	0.973 0	RMSE=0.059 3	〔56〕
ANN、RF	75种生物炭	Cd（Ⅱ）等5种	0.841 0	NRMSE=0.109 0	〔9〕
ANN、RSM	黑孜然	Pb（Ⅱ）	0.957 0	RMSE=0.557 0	〔60〕
ANN、ANFIS、MLR	红毛丹皮	Cu（Ⅱ）	0.900 0	RMSE=0.045 0	〔61〕
SVM	EDTA功能化生物炭	Pb（Ⅱ）	0.988 0		〔62〕
NN、RF、SVR	多种生物炭	Ni（Ⅱ）等8种	0.920 0	RMSE=9.920 0	〔16〕
QSA-ANN	44种生物炭	Cu（Ⅱ）等6种	0.960 0	RMSE=0.074 0	〔63〕
FCM-BPNN	24种生物质	Pb（Ⅱ）等6种	0.987 0	RMSE=0.036 0	〔64〕
KELM、Kriging	44种生物炭	Pb（Ⅱ）等6种	0.984 0	RMSE=0.049 0	〔19〕
6个多输入输出模型、MRA	椰枣种子	Pb（Ⅱ）等3种	0.992 3	RMSE=1.100 0	〔15〕
GP、ANFIS、CSA-LSSVM、ANFIS（PSO-ANFIS）	44种生物炭	Zn（Ⅱ）等6种	0.983 2	MSE=0.002 0	〔65〕

机器学习算法	生物炭种类	金属离子	模型最优精度		参考文献
机器学习算法	生物炭种类	金属离子	R ²	RMSE/NRMSE/MSE	参考文献
MRA	香蕉芯生物炭	As（Ⅳ）	0.820 0		〔59〕
ANN、RF	24种生物炭	Pb（Ⅱ）等6种	0.973 0	RMSE=0.059 3	〔56〕
ANN、RF	75种生物炭	Cd（Ⅱ）等5种	0.841 0	NRMSE=0.109 0	〔9〕
ANN、RSM	黑孜然	Pb（Ⅱ）	0.957 0	RMSE=0.557 0	〔60〕
ANN、ANFIS、MLR	红毛丹皮	Cu（Ⅱ）	0.900 0	RMSE=0.045 0	〔61〕
SVM	EDTA功能化生物炭	Pb（Ⅱ）	0.988 0		〔62〕
NN、RF、SVR	多种生物炭	Ni（Ⅱ）等8种	0.920 0	RMSE=9.920 0	〔16〕
QSA-ANN	44种生物炭	Cu（Ⅱ）等6种	0.960 0	RMSE=0.074 0	〔63〕
FCM-BPNN	24种生物质	Pb（Ⅱ）等6种	0.987 0	RMSE=0.036 0	〔64〕
KELM、Kriging	44种生物炭	Pb（Ⅱ）等6种	0.984 0	RMSE=0.049 0	〔19〕
6个多输入输出模型、MRA	椰枣种子	Pb（Ⅱ）等3种	0.992 3	RMSE=1.100 0	〔15〕
GP、ANFIS、CSA-LSSVM、ANFIS（PSO-ANFIS）	44种生物炭	Zn（Ⅱ）等6种	0.983 2	MSE=0.002 0	〔65〕

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