利用机器学习对水泥熟料相进行工业规模预测。

Sheikh Junaid Fayaz, Néstor Montiel-Bohórquez, Shashank Bishnoi, Matteo Romano, Manuele Gatti, N M Anoop Krishnan
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引用次数: 0

摘要

水泥年产量超过41亿吨,每年排放24亿吨二氧化碳,需要改进工艺控制。传统的模型,仅限于稳态条件下,缺乏预测熟料矿物学相的准确性。在这里,使用一个全面的两年工业数据集,我们开发的机器学习模型优于传统的Bogue方程,alite, belite和ferrite预测的平均绝对百分比误差分别为1.24%,6.77%和2.53%,而Bogue计算的平均绝对百分比误差分别为7.79%,22.68%和24.54%。我们的模型在不同的操作下保持稳健,并针对不确定性和罕见事件情景进行评估。通过事后可解释的算法,我们解释了熟料氧化物和相形成之间的层次关系,为其他黑箱模型的功能提供了见解。该框架可以实现水泥生产的实时优化,从而提供减少材料浪费和确保质量的途径,同时减少实际条件下的相关排放。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Industrial-scale prediction of cement clinker phases using machine learning.

Cement production exceeds 4.1 billion tonnes annually, emitting 2.4 billion tonnes of CO2 annually, necessitating improved process control. Traditional models, limited to steady-state conditions, lack predictive accuracy for clinker mineralogical phases. Here, using a comprehensive two-year industrial dataset, we develop machine learning models that outperform conventional Bogue equations with mean absolute percentage errors of 1.24%, 6.77%, and 2.53% for alite, belite, and ferrite prediction respectively, compared to 7.79%, 22.68%, and 24.54% for Bogue calculations. Our models remain robust under varying operations and are evaluated for uncertainty and rare-event scenarios. Through post hoc explainable algorithms, we interpret the hierarchical relationships between clinker oxides and phase formation, providing insights into the functioning of an otherwise black-box model. The framework can potentially enable real-time optimization of cement production, thereby providing a route toward reducing material waste and ensuring quality while reducing the associated emissions under real-world conditions.

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