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Optimizing Cu2+adsorption prediction in Undaria pinnatifida using machine learning and isotherm models

文献类型：期刊论文

作者Chen, Haoran3; Zhang, Rui3; Qu, Xiaohan3; Shan, Tifeng2; Wang, Yuhe1; Zhou, Rongbing3; Zhao, Shichao3
刊名JOURNAL OF HAZARDOUS MATERIALS
出版日期2025-07-15
卷号492页码:13
关键词Heavy metal removal Machine learning Traditional adsorption models Bioactive materials Explainable analysis
ISSN号0304-3894
DOI10.1016/j.jhazmat.2025.138202
通讯作者Zhang, Rui(rui.zhang@hdu.edu.cn)
英文摘要Algae are cost-effective bioadsorbents for heavy metal remediation, yet their potential is underutilized due to limitations in traditional adsorption models. This study integrates machine learning (ML) techniques with traditional models to predict the Cu2+ adsorption capacity by Undaria pinnatifida, enabling more efficient and targeted strategies for heavy metal removal. The study determined the relationship between bioactive compounds (mannitol, alginate, phlorotannins) content in different parts (blade, stipe, sporophyll) of algae and revealed a positive correlation between phlorotannins and Cu2+ adsorption capacity. The adsorption behavior of algal blades was best described by the Freundlich model (R2=0.9858), pseudo-second-order kinetic model (R2=0.9989), and thermodynamic model (R2=0.9912). These models suggest multilayer adsorption and confirm the spontaneous nature of the adsorption process. ML regression using factors such as temperature, initial concentration, time, and equilibrium concentration, with CatBoost providing the best predictions (R2=0.9883). Feature importance analysis (Shapley and Partial Dependence Plot) identified the initial concentration as the most influential factor affecting Cu2+ adsorption. This study presents a novel approach by combining traditional models and ML techniques to predict algal Cu2+ adsorption capacity. The findings highlight the potential of ML for accurate predictions and provide valuable insights for enhancing the utilization of algae in environmental pollution control.
WOS研究方向Engineering ; Environmental Sciences & Ecology
语种英语
WOS记录号WOS:001469800400001
出版者ELSEVIER
源URL[http://ir.qdio.ac.cn/handle/337002/201641]  
专题海洋研究所_实验海洋生物学重点实验室
通讯作者Zhang, Rui
作者单位1.Hangzhou Dianzi Univ, Sch Comp Sci, Hangzhou 310018, Peoples R China
2.Chinese Acad Sci, Inst Oceanol, Key Lab Breeding Biotechnol & Sustainable Aquacult, Qingdao, Peoples R China
3.Hangzhou Dianzi Univ, Coll Mat & Environm Engn, Hangzhou 310018, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Haoran,Zhang, Rui,Qu, Xiaohan,et al. Optimizing Cu2+adsorption prediction in Undaria pinnatifida using machine learning and isotherm models[J]. JOURNAL OF HAZARDOUS MATERIALS,2025,492:13.
APA Chen, Haoran.,Zhang, Rui.,Qu, Xiaohan.,Shan, Tifeng.,Wang, Yuhe.,...&Zhao, Shichao.(2025).Optimizing Cu2+adsorption prediction in Undaria pinnatifida using machine learning and isotherm models.JOURNAL OF HAZARDOUS MATERIALS,492,13.
MLA Chen, Haoran,et al."Optimizing Cu2+adsorption prediction in Undaria pinnatifida using machine learning and isotherm models".JOURNAL OF HAZARDOUS MATERIALS 492(2025):13.

入库方式： OAI收割

来源：海洋研究所

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