School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Department of Electronic and Information Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong SAR 999077, China.
Environ Sci Technol. 2023 Nov 21;57(46):17940-17949. doi: 10.1021/acs.est.2c06133. Epub 2023 Aug 25.
The utilization of steel slag for CO sequestration is an effective way to reduce carbon emissions. The reactivity of steel slag in CO 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 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 + AlO", "FeO", and "MnO" varied from 50-55%, 10-15%, 30-35%, and <5%, respectively.
利用钢渣进行 CO2 捕集是减少碳排放的有效方法。钢渣在 CO2 捕集中的反应活性主要取决于材料和工艺参数。然而，由于对工艺参数的评价不同以及对材料参数的研究不足，在实际应用中存在许多难题。本研究通过机器学习与 SHapley Additive exPlanations (SHAP) 对 318 个样本进行了调查，以研究 12 个因素对钢渣碳酸化反应性的交互影响。建立了多层感知机（MLP）、随机森林和支持向量回归模型来预测钢渣的浆体相 CO2 捕集。MLP 模型在预测能力和泛化能力方面表现良好，同时具有全面的可解释性。SHAP 结果表明，工艺参数的影响大于材料参数的影响。有趣的是，通过 SHAP 分析发现钢渣中的磁铁矿相对钢渣碳酸化具有积极影响。结合以往文献，提出了钢渣的碳酸化机理。基于 SHAP 的定量分析表明，当“CaO+MgO”、“SiO+AlO”、“FeO”和“MnO”的质量分数分别为 50-55%、10-15%、30-35%和<5%时，钢渣具有良好的碳酸化反应活性。
