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Machine learning assisted adsorption performance evaluation of biochar on heavy metal

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Heavy metals(HMs)represent pervasive and highly toxic environmental pollutants,known for their long latency periods and high toxicity levels,which pose significant challenges for their removal and degradation.Therefore,the removal of heavy metals from the environment is crucial to ensure the water safety.Biochar materials,known for their intricate pore structures and abundant oxygen-containing functional groups,are frequently harnessed for their effectiveness in mitigating heavy metal contamination.However,conventional tests for optimizing biochar synthesis and assessing their heavy metal adsorption capabilities can be both costly and tedious.To address this challenge,this paper proposes a data-driven machine learning(ML)approach to identify the optimal biochar preparation and adsorption reaction conditions,with the ultimate goal of maximizing their adsorption capacity.By utilizing a data set comprising 476 instances of heavy metal absorption by biochar,seven classical integrated models and one stacking model were trained to rapidly predict the efficiency of heavy metal adsorption by biochar.These predictions were based on diverse physicochemical properties of biochar and the specific adsorption reaction conditions.The results demonstrate that the stacking model,which integrates multiple algorithms,allows for training with fewer samples to achieve higher prediction accuracy and improved generalization ability.

Machine learningBiocharHeavy metalAdsorption efficiency

Qiannan Duan、Pengwei Yan、Yichen Feng、Qianru Wan、Xiaoli Zhu

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Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity,College of Urban and Environmental Sciences,Northwest University,Xi'an 710127,China

Department of Environment Science,Shaanxi Normal University,Xi'an 710062,China

National Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaKey Research and Development Program of Shaanxi Province(China)Key Research and Development Program of Shaanxi Province(China)Key Research and Development Program of Shaanxi Province(China)Agricultural Technology Innovation Driven Program of Shaanxi Province(China)

2021YFC1808902423075462019NY2002020ZDLNY06-062020ZDLNY07-10NYKJ-2022-XA-02

2024

10.1007/s11783-024-1815-4

环境科学与工程前沿
高等教育出版社

环境科学与工程前沿

影响因子：0.545
ISSN：2095-2201
年,卷(期)：2024.18(5)