Prof. Chongchong Qi
School of Resources and Safety Engineering, Central South University, China
Speech Title: 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 in thermal plant, 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 presentation will introduce a novel methodology for the rapid reactivity identification of CFA. The random forest regression models optimized by artificial bee colony (ABC) were established. 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 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 the energy industry.