Industrial-scale Prediction of Cement Clinker Phases using Machine Learning

TL;DR

This paper introduces a machine learning-based digital twin framework that accurately predicts clinker mineralogy in real-time, enabling improved process control, quality, and sustainability in cement manufacturing.

Contribution

It presents a novel machine learning approach trained on two years of industrial data to predict clinker phases with high accuracy under dynamic conditions.

Findings

Achieved high prediction accuracy for clinker phases

Demonstrated robustness under varying operating conditions

Enabled real-time process optimization

Abstract

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.