用机器学习精准预测水泥熟料成分，助力低碳生产
Industrial-scale Prediction of Cement Clinker Phases using Machine Learning
- 基于两年工业数据构建预测模型，输入少、精度高
- 对主要熟料相的预测准确率显著优于传统方法
- 可解释性强，适合工厂实现实时质量优化
水泥年产量超41亿吨，每年排放2.4亿吨二氧化碳，面临质量控制与工艺优化的重大挑战。传统工艺模型受限于稳态假设，难以预测矿物相变化；而现代工厂运行动态复杂，亟需实时质量评估。本文利用某工业化水泥厂两年运行数据，构建机器学习框架，实现从工艺数据精准预测熟料矿物组成。模型在仅需少量输入参数的情况下，对主要熟料相的预测精度达到前所未有的水平，并在不同工况下表现稳健。通过事后可解释算法，揭示了熟料氧化物与相形成间的层级关系，解析了原本黑箱模型的运作逻辑。该数字孪生框架有望实现水泥生产的实时优化，减少材料浪费与排放，推动可持续制造。本方法代表工业过程控制的重要进展，具备规模化应用潜力。
原文摘要 · Abstract (English)
Cement production, exceeding 4.1 billion tonnes and contributing 2.4 tonnes of CO2 annually, faces critical challenges in quality control and process optimization. While traditional process models for cement manufacturing are confined to steady-state conditions with limited predictive capability for mineralogical phases, modern plants operate under dynamic conditions that demand real-time quality assessment. Here, exploiting a comprehensive two-year operational dataset from an industrial cement plant, we present a machine learning framework that accurately predicts clinker mineralogy from process data. Our model achieves unprecedented prediction accuracy for major clinker phases while requiring minimal input parameters, demonstrating robust performance under varying operating conditions. 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. This digital twin 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 plant conditions. Our approach represents a significant advancement in industrial process control, offering a scalable solution for sustainable cement manufacturing.
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