生物炭对重金属的机器学习辅助吸附性能评估

IF 6.1 2区 环境科学与生态学 Q2 ENGINEERING, ENVIRONMENTAL
Qiannan Duan, Pengwei Yan, Yichen Feng, Qianru Wan, Xiaoli Zhu
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引用次数: 0

摘要

重金属(HMs)是一种普遍存在的高毒性环境污染物,以潜伏期长、毒性大而著称,这给重金属的去除和降解带来了巨大挑战。因此,清除环境中的重金属对确保水质安全至关重要。生物炭材料因其复杂的孔隙结构和丰富的含氧官能团而闻名,经常被用来有效缓解重金属污染。然而,用于优化生物炭合成和评估其重金属吸附能力的传统测试既昂贵又繁琐。为了应对这一挑战,本文提出了一种数据驱动的机器学习(ML)方法,以确定最佳的生物炭制备和吸附反应条件,最终实现最大化吸附能力的目标。通过利用由 476 个生物炭吸附重金属实例组成的数据集,训练了七个经典综合模型和一个堆叠模型,以快速预测生物炭吸附重金属的效率。这些预测基于生物炭的不同理化特性和特定的吸附反应条件。结果表明,堆叠模型集成了多种算法,可以用更少的样本进行训练,从而获得更高的预测精度和更强的泛化能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Machine learning assisted adsorption performance evaluation of biochar on heavy metal

Machine learning assisted adsorption performance evaluation of biochar on heavy metal

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.

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来源期刊
Frontiers of Environmental Science & Engineering
Frontiers of Environmental Science & Engineering ENGINEERING, ENVIRONMENTAL-ENVIRONMENTAL SCIENCES
CiteScore
10.90
自引率
12.50%
发文量
988
审稿时长
6.1 months
期刊介绍: Frontiers of Environmental Science & Engineering (FESE) is an international journal for researchers interested in a wide range of environmental disciplines. The journal''s aim is to advance and disseminate knowledge in all main branches of environmental science & engineering. The journal emphasizes papers in developing fields, as well as papers showing the interaction between environmental disciplines and other disciplines. FESE is a bi-monthly journal. Its peer-reviewed contents consist of a broad blend of reviews, research papers, policy analyses, short communications, and opinions. Nonscheduled “special issue” and "hot topic", including a review article followed by a couple of related research articles, are organized to publish novel contributions and breaking results on all aspects of environmental field.
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