Interpretation and Prediction of the CO<sub>2</sub> Sequestration of Steel Slag by Machine Learning
Bingyang He, Xingyu Zhu, Zhizhi Cang, Yang Liu, Yuxin Lei, Zhaohou Chen, Yanlin Wang, Yongchao Zheng, Daqiang Cang, Lingling Zhang
Abstract
The utilization of steel slag for CO 2 sequestration is an effective way to reduce carbon emissions. The reactivity of steel slag in CO 2 sequestration depends mainly on material and process parameters. However, there are many puzzles in regard to practical applications due to the different evaluations of process parameters and the lack of investigation of material parameters. In this study, 318 samples were collected to investigate the interactive influence of 12 factors on the carbonation reactivity of steel slag by machine learning with SHapley Additive exPlanations (SHAP). Multilayer perceptron (MLP), random forest, and support vector regression models were built to predict the slurry-phase CO 2 sequestration of steel slag. The MLP model performed well in terms of prediction ability and generalization with comprehensive interpretability. The SHAP results showed that the impact of the process parameters was greater than that of the material parameters. Interestingly, the iron ore phase of steel slag was revealed to have a positive effect on steel slag carbonation by SHAP analysis. Combined with previous literature, the carbonation mechanism of steel slag was proposed. Quantitative analysis based on SHAP indicated that steel slag had good carbonation reactivity when the mass fractions of “CaO + MgO”, “SiO 2 + Al 2 O 3 ”, “Fe 2 O 3 ”, and “MnO” varied from 50–55%, 10–15%, 30–35%, and <5%, respectively.