Key Laboratory of Eco-environments in the Three Gorges Reservoir Region, Ministry of Education, College of Environmental and Ecology, Chongqing University, Chongqing, 400044, China.
Key Laboratory of Eco-environments in the Three Gorges Reservoir Region, Ministry of Education, College of Environmental and Ecology, Chongqing University, Chongqing, 400044, China; State Key Laboratory of Urban Water Resource and Environment, School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin, 150090, China.
Chemosphere. 2024 Apr;354:141584. doi: 10.1016/j.chemosphere.2024.141584. Epub 2024 Mar 7.
Carbonaceous materials are commonly used as adsorbents for heavy metals. The determination of the adsorption capacity needs time and energy, and the key factors affecting the adsorption capacity have not been determined. Therefore, a new and efficient method is needed to predict the adsorption capacity and explore the decisive factors in the adsorption process. In this study, three tree-based machine learning models (i.e., random forest, gradient boosting decision tree, and extreme gradient boosting) were developed to predict the adsorption capacity of eight heavy metals (i.e., As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) on activated carbons, biochars, and carbon nanotubes using 3674 data points extracted from 151 journal articles. After a comprehensive comparison, the gradient boosting decision tree had the best performance for a combined model based on all data (R = 0.9707, RMSE = 0.1420). Moreover, independent models were developed for three datasets classified by the adsorbent and eight datasets classified by the heavy metals. In addition, a graphical user interface was built to predict the adsorption capacity of heavy metals. This study provides a novel strategy and convenient tool for the removal of heavy metals and can help to improve the removal efficiency of heavy metals to build a healthier world.
碳质材料通常被用作重金属的吸附剂。吸附容量的测定既费时又费力，而且影响吸附容量的关键因素尚未确定。因此，需要一种新的、有效的方法来预测吸附容量，并探索吸附过程中的决定性因素。在这项研究中，我们开发了三种基于树的机器学习模型（即随机森林、梯度提升决策树和极端梯度提升），以预测 3674 个数据点（从 151 篇期刊文章中提取）在预测 8 种重金属（即 As、Cd、Cr、Cu、Hg、Ni、Pb 和 Zn）在活性炭、生物炭和碳纳米管上的吸附容量。经过综合比较，梯度提升决策树在基于所有数据的组合模型中表现最好（R=0.9707，RMSE=0.1420）。此外，我们还为按吸附剂分类的三个数据集和按重金属分类的八个数据集分别开发了独立的模型。此外，我们还构建了一个图形用户界面来预测重金属的吸附容量。这项研究为重金属的去除提供了一种新的策略和便捷的工具，并有助于提高重金属的去除效率，以构建一个更健康的世界。
