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

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.