Rapid identification of reactivity for the efficient recycling of coal fly ash: Hybrid machine learning modeling and interpretation

Abstract

As the main solid waste produced by coal combustion, the large accumulation of coal fly ash (CFA) causes serious environmental pollution and resource waste. Whether CFA can be recycled depends on its reactivity, which in turns can be represented by its amorphous content. This paper established random forest regression models optimized by artificial bee colony (ABC) for rapid screening of CFA, based on the correlation between chemical composition and amorphous phase. The study evaluated the model using correlation coefficient, r-square, root mean square error, and mean absolute error, giving results of testing set of 0.773, 0.477, 6.542, and 5.279. Feature importance and permutation importance were used to measure feature contribution. Partial dependence plots, Shapley additive explanations, and local interpretable model-agnostic explanations were also used to give global and local interpretation of the model performance. The overall results showed that Al2O3, MgO and CaO had the greatest influence on the amorphous content within CFA. When the mass fractions of Al2O3, MgO and CaO varied from 17.5% to 22.5%, 2%–4% and 10–15%, the mass fraction of amorphous phase reached the highest. For different CFA samples, chemical composition played a different role in determining the amorphous content. The post-analysis of the model provided some reference for promoting CFA recycling. The above results proved that the established model had good robustness and generalization capability, which can effectively determine the potential of CFA as supplementary cementitious materials to promote the cleaner production of cement or geopolymer resources.