基于集合学习的废水中铀的生物炭吸附预测及关键影响参数反演

IF 3.9 3区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES
Toxics Pub Date : 2024-09-26 DOI:10.3390/toxics12100698
Zening Qu, Wei Wang, Yan He
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引用次数: 0

摘要

随着工业化的快速发展,重金属废水处理问题日益严重,对环境和人类健康构成了严重威胁。生物炭在废水处理领域具有巨大的应用潜力,但不同生物质来源和实验条件制备的生物炭具有不同的理化性质,导致其对铀的吸附能力存在差异,限制了其在废水处理中的广泛应用。因此,迫切需要深入探索和优化生物炭的关键参数设置,以显著提高其吸附能力。本文基于现有的废水处理实验数据,将 SCN 的非线性映射能力与 Adaboost 算法的集合学习优势相结合。模型的准确性通过判定系数(R2)和误差率等指标进行评估。结果发现,与单独的 SCN 模型相比,Adaboost-SCN 模型在预测准确度、精确度、模型稳定性和泛化能力等方面都具有显著优势。为了进一步提高模型的性能,本文将 Adaboost-SCN 与最大信息系数(MIC)、随机森林(RF)和能量谷优化器(EVO)特征选择方法相结合,构建了三种模型,即 MIC-Adaboost-SCN、RF-Adaboost-SCN 和 EVO-Adaboost-SCN。结果表明,添加了特征选择的预测模型在各项评价指标上都明显优于未添加特征选择的 Adaboost-SCN 模型,其中 EVO 对特征选择的影响最为显著。最后,通过对关键参数的反演研究,探讨了生物炭吸附性能与生产参数之间的相关性,并提出了改善吸附性能的最佳参数区间。为生物炭在废水处理领域的广泛应用提供了有力支持,有助于解决重金属废水处理这一亟待解决的环境问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Biochar Adsorption of Uranium in Wastewater and Inversion of Key Influencing Parameters Based on Ensemble Learning.

With the rapid development of industrialization, the problem of heavy metal wastewater treatment has become increasingly serious, posing a serious threat to the environment and human health. Biochar shows great potential for application in the field of wastewater treatment; however, biochars prepared from different biomass sources and experimental conditions have different physicochemical properties, resulting in differences in their adsorption capacity for uranium, which limits their wide application in wastewater treatment. Therefore, there is an urgent need to deeply explore and optimize the key parameter settings of biochar to significantly improve its adsorption capacity. This paper combines the nonlinear mapping capability of SCN and the ensemble learning advantage of the Adaboost algorithm based on existing experimental data on wastewater treatment. The accuracy of the model is evaluated by metrics such as coefficient of determination (R2) and error rate. It was found that the Adaboost-SCN model showed significant advantages in terms of prediction accuracy, precision, model stability and generalization ability compared to the SCN model alone. In order to further improve the performance of the model, this paper combined Adaboost-SCN with maximum information coefficient (MIC), random forest (RF) and energy valley optimizer (EVO) feature selection methods to construct three models, namely, MIC-Adaboost-SCN, RF-Adaboost-SCN and EVO-Adaboost-SCN. The results show that the prediction model with added feature selection is significantly better than the Adaboost-SCN model without feature selection in each evaluation index, and EVO has the most significant effect on feature selection. Finally, the correlation between biochar adsorption properties and production parameters was discussed through the inversion study of key parameters, and optimal parameter intervals were proposed to improve the adsorption properties. Providing strong support for the wide application of biochar in the field of wastewater treatment helps to solve the urgent environmental problem of heavy metal wastewater treatment.

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来源期刊
Toxics
Toxics Chemical Engineering-Chemical Health and Safety
CiteScore
4.50
自引率
10.90%
发文量
681
审稿时长
6 weeks
期刊介绍: The Journal accepts papers describing work that furthers our understanding of the exposure, effects, and risks of chemicals and materials in humans and the natural environment as well as approaches to assess and/or manage the toxicological and ecotoxicological risks of chemicals and materials. The journal covers a wide range of toxic substances, including metals, pesticides, pharmaceuticals, biocides, nanomaterials, and polymers such as micro- and mesoplastics. Toxics accepts papers covering: The occurrence, transport, and fate of chemicals and materials in different systems (e.g., food, air, water, soil); Exposure of humans and the environment to toxic chemicals and materials as well as modelling and experimental approaches for characterizing the exposure in, e.g., water, air, soil, food, and consumer products; Uptake, metabolism, and effects of chemicals and materials in a wide range of systems including in-vitro toxicological assays, aquatic and terrestrial organisms and ecosystems, model mammalian systems, and humans; Approaches to assess the risks of chemicals and materials to humans and the environment; Methodologies to eliminate or reduce the exposure of humans and the environment to toxic chemicals and materials.
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