砷酸盐
吸附
铬酸盐转化膜
均方误差
针铁矿
化学
试验装置
曲面(拓扑)
最大值和最小值
生物系统
计算机科学
数学
人工智能
铬
统计
物理化学
砷
有机化学
几何学
数学分析
生物
作者
Kai Chen,Chuling Guo,Chaoping Wang,Shuangfeng Zhao,Butian Xiong,Guining Lu,John R. Reinfelder,Zhi Dang
标识
DOI:10.1016/j.watres.2024.121580
摘要
This study aimed to develop surface complexation modeling-machine learning (SCM-ML) hybrid model for chromate and arsenate adsorption on goethite. The feasibility of two SCM-ML hybrid modeling approaches was investigated. Firstly, we attempted to utilize ML algorithms and establish the parameter model, to link factors influencing the adsorption amount of oxyanions with optimized surface complexation constants. However, the results revealed the optimized chromate or arsenate surface complexation constants might fall into local extrema, making it unable to establish a reasonable mapping relationship between adsorption conditions and surface complexation constants by ML algorithms. In contrast, species-informed models were successfully obtained, by incorporating the surface species information calculated from the unoptimized SCM with the adsorption condition as input features. Compared with the optimized SCM, the species-informed model could make more accurate predictions on pH edges, isotherms, and kinetic data for various input conditions (for chromate: root mean square error (RMSE) on test set = 5.90%; for arsenate: RMSE on test set = 4.84%). Furthermore, the utilization of the interpretable formula based on Local Interpretable Model-Agnostic Explanations (LIME) enabled the species-informed model to provide surface species information like SCM. The species-informed SCM-ML hybrid modeling method proposed in this study has great practicality and application potential, and is expected to become a new paradigm in surface adsorption model.
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