用机器学习解释和预测二氧化碳2钢渣的封存
Bingyang He1, Xingyu Zhu2, Zhizhi Cang3
1School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Environmental science & technology
|August 25, 2023
概括
钢渣有效地将二氧化碳 (CO2) 隔离起来,以减少排放. 机器学习确定了过程参数比物质因素对最佳钢炭化有更大的影响.
科学领域:
- 环境科学 环境科学
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 用钢来捕获二氧化碳是一种有效的减轻碳排放的方法.
- 实际应用面临挑战,原因是过程参数的评估不同,材料参数的研究有限.
研究的目的:
- 研究12种材料和工艺因素对钢碳化反应性的相互作用影响.
- 使用机器学习模型预测泥阶段的二氧化碳封存.
- 为了阐明钢的碳化机制.
主要方法:
- 收集了318个钢样本进行分析.
- 采用机器学习模型,包括多层感知器 (MLP),随机森林和支持向量回归.
- 利用夏普利添加式解释 (SHAP) 进行模型解释性和因素重要性分析.
主要成果:
- 该MLP模型表现出强大的预测能力和对二氧化碳封存的概括性.
- SHAP分析显示,工艺参数对碳化反应性的影响比材料参数更大.
- 在钢中的铁矿石阶段被确定为积极影响碳化.
结论:
- 在特定的质量分数下,钢渣具有良好的碳化反应性:CaO + MgO (50-55%),SiO2 + Al2O3 (10-15%),Fe2O3 (30-35%) 和MNO (<5%).
- 一个拟议的钢碳化机制将实验发现与SHAP分析相结合.
- 该研究为优化在二氧化碳封存中的钢铁渣利用提供了定量见解.
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