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.

翻译：水泥年产量超过41亿吨，每年产生24亿吨二氧化碳排放，其质量控制和工艺优化面临严峻挑战。传统水泥制造过程模型局限于稳态条件，对矿物相的预测能力有限，而现代工厂在动态条件下运行，需要实时质量评估。本研究利用某工业水泥工厂为期两年的综合运行数据集，提出一种机器学习框架，可从过程数据中准确预测熟料矿物组成。该模型在仅需最少输入参数的情况下，对主要熟料相实现了前所未有的预测精度，并在不同运行条件下表现出稳健性能。通过事后可解释性算法，我们阐释了熟料氧化物与相形成之间的层次关系，为这一黑箱模型的功能机制提供了见解。该数字孪生框架有望实现水泥生产的实时优化，从而为减少材料浪费、保障产品质量，并在实际工厂条件下降低相关排放提供可行路径。我们的方法代表了工业过程控制领域的重大进展，为可持续水泥制造提供了可扩展的解决方案。