An Assessment of the Mobility of Toxic Elements in Coal Fly Ash Using the Featured BPNN Model

Abstract

This study aims to propose a novel backpropagation neural network (BPNN) featured with sequential forward selection (SFS), named the BPNN_s model, to master the leaching characteristics of toxic elements (TEs) in coal fly ash (CFA). A total of 400 datasets and 54 features are involved to predict the fractions of TEs. The determination coefficient (R2), root mean square error (RMSE) and variance accounted for (VAF) and Willmott’s index (WI) are used to validate the BPNN_s, and its predictive performance is compared with the other three models, including the unified BPNN (BPNN_u), the adaptive boosting (AdaBoost) and the random forest (RF) models. The results indicate that the BPNN_s outperforms others in predicting the fractions of TEs, and feature selection is an imperative step for developing a model. Moreover, the features selected with SFS suggest that the influence of the element properties is more significant than that of the chemical properties as well as the concentration on predicting the fractions of TEs. Atomic weight is found to be the most critical feature in the prediction through a shapely additive explanations (SHAP) analysis. This study helps to assess the TEs’ mobility rapidly and accurately and provides a foundation for obtaining insights into the relationship between the features and the fractions of TEs.