Open AccessArticle
Automated Machine Learning-Based Prediction of the Effects of Physicochemical Properties and External Experimental Conditions on Cadmium Adsorption by Biochar
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Shuoyang Wang, Xiangyu Song, Jicheng Duan, Shuo Li, Dangdang Gao, Jia Liu, Fanjing Meng, Wen Yang, Shixin Yu, Fangshu Wang, Jie Xu, Siyi Luo, Fangchao Zhao and Dong Chen
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Abstract
Biochar serves as an effective adsorbent for the heavy metal cadmium, with its performance significantly influenced by its physicochemical properties and various environmental features. Traditional machine learning models, though adept at managing complex multi-feature relationships, rely heavily on expertise in feature engineering and
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Biochar serves as an effective adsorbent for the heavy metal cadmium, with its performance significantly influenced by its physicochemical properties and various environmental features. Traditional machine learning models, though adept at managing complex multi-feature relationships, rely heavily on expertise in feature engineering and hyperparameter optimization. To address these issues, this study employs an automated machine learning (AutoML) approach, automating feature selection and model optimization, coupled with an intuitive online graphical user interface, enhancing accessibility and generalizability. Comparative analysis of four AutoML frameworks (TPOT, FLAML, AutoGluon, H
2O AutoML) demonstrated that H
2O AutoML achieved the highest prediction accuracy (R
2 = 0.918). Key features influencing adsorption performance were identified as initial cadmium concentration (23%), stirring rate (14.7%), and the biochar H/C ratio (9.7%). Additionally, the maximum adsorption capacity of the biochar was determined to be 105 mg/g. Optimal production conditions for biochar were determined to be a pyrolysis temperature of 570–800 °C, a residence time of ≥2 h, and a heating rate of 3–10 °C/min to achieve an H/C ratio of <0.2. An online graphical user interface was developed to facilitate user interaction with the model. This study not only provides practical guidelines for optimizing biochar but also introduces a novel approach to modeling using AutoML.
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