粉煤灰
焚化
城市固体废物
废物管理
重金属
焚烧炉底灰
环境科学
废物处理
金属
材料科学
冶金
化学
工程类
底灰
环境化学
作者
Lisheng Guo,Xiangmin Xu,Qing Wang,Junboum Park,Haomin Lei,Lu Zhang,Xinhai Wang
标识
DOI:10.1016/j.jhazmat.2024.133682
摘要
Geopolymer is an environmentally friendly solidification/stabilization (S/S) binder, exhibiting significant potential for immobilizing heavy metals in municipal solid waste incineration fly ash (MSWIFA). However, due to the diversity in geopolymer raw materials and heavy metal properties, predicting the heavy metal immobilization rate proves to be challenging. In order to enhance the application of geopolymers in immobilizing heavy metals in MSWIFA, a universal method is required to predict the heavy metal immobilization rate. Therefore, this study employs machine learning to predict the heavy metal immobilization rate in S/S of MSWIFA by geopolymers. A gradient boosting regression (GB) model with superior performance (R2 = 0.9214) was obtained, and a graphical user interface (GUI) software was developed to facilitate the convenient accessibility of researchers. The feature categories influencing heavy metal immobilization rate are ranked in order of importance as heavy metal properties > geopolymer raw material properties > curing conditions > alkali activator properties. This study facilitates the rapid prediction, improvement, and optimization of heavy metal immobilization in S/S of MSWIFA by geopolymers, and also provides a theoretical basis for the resource utilization of industrial solid waste, contributing to the environmental protection. Municipal solid waste incineration fly ash (MSWIFA) contains a significant amount of heavy metals, and if not treated before landfill disposal, it poses a risk of contaminating surrounding water and soil, thereby threatening ecological systems and human health. Geopolymer, as an innovative solidification/stabilization (S/S) material, proves effective in S/S of heavy metals in MSWIFA. Machine learning regression algorithms can effectively predict the heavy metal immobilization rate in S/S of MSWIFA by geopolymers, assisting researchers and engineers in optimizing experimental designs, thereby facilitating the resource utilization of industrial solid waste and environmental protection.
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