Modeling operational cement rotary kiln variables with explainable artificial intelligence methods - a "conscious lab" development

• Published in: Particulate science and technology Vol. 41; no. 5; pp. 715 - 724
• Main Authors: Fatahi, Rasoul, Nasiri, Hamid, Homafar, Arman, Khosravi, Rasoul, Siavoshi, Hossein, Chehreh Chelgani, Saeed
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 04.07.2023; Taylor & Francis Ltd
• Subjects: Artificial intelligence; cement industry; Cement plants; Clinker; digitalization; Digitization; Energy consumption; Explainable artificial intelligence; Feed rate; Kilns; machine learning; Mineral Processing; Mineralteknik; Modelling; Rotary kiln
• ISSN: 0272-6351; 1548-0046; 1548-0046
• DOI: 10.1080/02726351.2022.2135470

Digitalizing cement production plants to improve operation parameters' control might reduce energy consumption and increase process sustainabilities. Cement production plants are one of the extremest CO 2 emissions, and the rotary kiln is a cement plant's most energy-consuming and energy-wasting unit. Thus, enhancing its operation assessments adsorb attention. Since many factors would affect the clinker production quality and rotary kiln efficiency, controlling those variables is beyond operator capabilities. Constructing a conscious-lab "CL" (developing an explainable artificial intelligence "EAI" model based on the industrial operating dataset) can potentially tackle those critical issues, reduce laboratory costs, save time, improve process maintenance and help for better training operators. As a novel approach, this investigation examined extreme gradient boosting (XGBoost) coupled with SHAP (SHapley Additive exPlanations) "SHAP-XGBoost" for the modeling and prediction of the rotary kiln factors (feed rate and induced draft fan current) based on over 3,000 records collected from the Ilam cement plant. SHAP illustrated the relationships between each record and variables with the rotary kiln factors, demonstrated their correlation magnitude, and ranked them based on their importance. XGBoost accurately (R-square 0.96) could predict the rotary kiln factors where results showed higher exactness than typical EAI models.