Evaluating the metal recovery potential of coal fly ash based on sequential extraction and machine learning

Mengting Wu; Chongchong Qi; Qiusong Chen; Hui Liu

doi:10.1016/j.envres.2023.115546

Evaluating the metal recovery potential of coal fly ash based on sequential extraction and machine learning

Environ Res. 2023 May 1:224:115546. doi: 10.1016/j.envres.2023.115546. Epub 2023 Feb 22.

Authors

Mengting Wu¹, Chongchong Qi², Qiusong Chen¹, Hui Liu³

Affiliations

¹ School of Resources and Safety Engineering, Central South University, Changsha, 410083, China.
² School of Resources and Safety Engineering, Central South University, Changsha, 410083, China; School of Metallurgy and Environment, Central South University, Changsha, 410083, China. Electronic address: chongchong.qi@csu.edu.cn.
³ School of Metallurgy and Environment, Central South University, Changsha, 410083, China.

PMID: 36828251
DOI: 10.1016/j.envres.2023.115546

Abstract

Given the depletion of metal resources and the potential leaching of toxic elements from solid waste, secondary recovery of metal from solid waste is essential to achieve coordinated development of resources and the environment. In this study, hybrid models combining the gradient boosting decision tree and particle swarm optimization algorithm were constructed and compared based on two different datasets. Additionally, a new, quantitative evaluation index for metal recovery potential (MRP) was proposed. The results showed that the model constructed using more elemental properties could more accurately predict metal fractions in coal fly ash (CFA) with an R² value of 0.88 achieved on the testing set. The MRP index revealed that the DAT sample had the greatest recovery potential (MRP = 43,311.70). Ca was easier to recover due to its high concentration and presence mostly in soluble fractions. Model post-analysis highlighted that the elemental properties and total concentrations generally exerted a greater influence on the metal fractions. The innovative evaluation strategy based on machine learning and sequential extraction presented in this work provides an important reference for maximizing metal recovery from CFA to achieve environmental and economic benefits with the goal of sustainable development.

Keywords: CFA; Fraction; MRP; Machine learning; Metal recovery.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Carbon
Coal
Coal Ash*
Incineration
Machine Learning
Metals / analysis
Metals, Heavy* / analysis
Solid Waste / analysis

Substances

Coal Ash
Solid Waste
Metals
Metals, Heavy
Coal
Carbon