文献类型：期刊文献

英文题名：Application of machine learning in prediction of Pb2+ adsorption of biochar prepared by tube furnace and fluidized bed

作者：Huang, Wei[1,2]; Wang, Liang[3]; Zhu, JingJing[1]; Dong, Lu[1,4]; Hu, Hongyun[1,4]; Yao, Hong[1]; Wang, LinLing[5]; Lin, Zhong[6,7]

机构：[1] State Key Laboratory of Coal Combustion, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China; [2] Faculty of Engineering, China University of Geosciences, Wuhan, 430074, China; [3] China Power Hua Chuang [Suzhou] Electricity Technology Research Company Co., Ltd., Suzhou, 215125, China; [4] Research Institute, Huazhong University of Science and Technology in Shenzhen, Wuhan, 430074, China; [5] School of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China; [6] Faculty of Chemistry and Environmental Science, Guangdong Ocean University, Zhanjiang, 524088, China; [7] Shenzhen Research Institute of Guangdong Ocean University, Shenzhen, 518108, China

年份：2024

卷号：31

期号：18

起止页码：27286

外文期刊名：Environmental Science and Pollution Research

收录：EI(收录号：20241215790726)、Scopus(收录号：2-s2.0-85188152361)

语种：英文

外文关键词：Chemicals removal (water treatment) - Data mining - Fluidized beds - Forestry - Heavy metals - Machine learning - Pyrolysis - Regression analysis

外文摘要：Abstract: Data mining by machine learning (ML) has recently come into application in heavy metals purification from wastewater, especially in exploring lead removal by biochar that prepared using tube furnace (TF-C) and fluidized bed (FB-C) pyrolysis methods. In this study, six ML models including Random Forest Regression (RFR), Gradient Boosting Regression (GBR), Support Vector Regression (SVR), Kernel Ridge Regression (KRR), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LGBM) were employed to predict lead adsorption based on a dataset of 1012 adsorption experiments, comprising 422 TF-C groups from our experiments and 590 FB-C groups from literatures. The XGB model showed superior accuracy and predictive performance for adsorption, achieving R2 values for TF-C (0.992) and FB-C (0.981), respectively. Contrasting inferior results were observed in other models, including RF (0.962 and 0.961), GBR (0.987 and 0.975), SVR (0.839 and 0.763), KRR (0.817 and 0.881), and LGBM (0.975 and 0.868). Additionally, a hybrid dataset combining both biochars in Pb adsorption also indicated high accuracy (0.972) as obtained from XGB model. The investigation revealed that the influence of char characteristics and adsorption conditions on Pb adsorption differs between the two biochar. Specific char characteristics, particularly nitrogen content, significantly influence lead adsorption in both biochar. Interestingly, the influence of pyrolysis temperature (PT) on lead adsorption is found to be greater for TF-C than for FB-C. Consequently, careful consideration of PT is crucial when preparing TF-C biochar. These findings offer practical guidance for optimizing biochar preparation conditions during heavy metal removal from wastewater. Graphical Abstract: (Figure presented.). ? The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.