Quality Control of Cement Clinker through Operating Condition Classification and Free Calcium Oxide Content Prediction

Abstract

Recent advances in artificial intelligence (AI) technologies such as deep learning open up new opportunities for various industries, such as cement manufacturing, to transition from traditional human-aided manually controlled production processes to the modern era of “intelligentization”. More and more practitioners have started to apply machine learning methods and deploy practical applications throughout the production process to automate manufacturing activities and optimize product quality. In this work, we employ machine learning methods to perform effective quality control for cement production through monitoring and predicting the density of free calcium oxide (f-CaO) in cement clinker. Based upon the control data measured and collected within the distributed control system (DCS) of cement production plants and the laboratory measurements of the density of free lime in cement clinker, we are able to train effective models to stabilize the cement production process and optimize the quality of cement clinker. We report the details of the methods used and illustrate the superiority and benefits of the adopted machine learning-based approaches.

Keywords

• machine learning
• XGBoost
• image classification
• operating condition recognition
• free calcium oxide (f-CaO) content prediction
• cementclinker quality