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Cement kiln safety and performance improvement based on machine learning predictive analytics

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Abstract

Occupational health and safety has top priority within the cement industry. The preheating tower with its series of installed cyclones is essential in the cement kiln production process and it is considered among the most dangerous places in a cement plant. Coatings and blockages can often occur in the cyclone preheaters of rotary kiln plants for burning cement clinker. These wall build-ups disturb and/or block the process downward flow of hot kiln feed and the upward flow of hot kiln exhaust gases. Actually, our research aims to use process prediction by operating the digital transformation through a 4.0 tool for monitoring and analyzing temperature and pressure in real time. This tool monitors temperature and pressure using sensors that transform the data into a computer platform for real-time analysis and predicts failures according to a predictive model to prevent the occurrence of preheater cyclone blockages. This new technology will help to further improve occupational safety, increases the efficiency of industrial processes, and increases productivity.

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Acknowledgements

The authors would like to thank all the stakeholders of this project and particularly the engineers and technicians of the cement plant where our research was conducted.

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Authors and Affiliations

  1. Laboratory of Innovation and Systems Engineering, Mechanical and Structural Engineering Department, ENSAM, Moulay Ismail University, Meknes, Morocco

    Mohammed Toum Benchekroun, Smail Zaki & Mohamed Aboussaleh

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  1. Mohammed Toum Benchekroun
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  2. Smail Zaki
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  3. Mohamed Aboussaleh
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Correspondence to Smail Zaki.

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Benchekroun, M.T., Zaki, S. & Aboussaleh, M. Cement kiln safety and performance improvement based on machine learning predictive analytics. Int J Adv Manuf Technol 125, 5267–5277 (2023). https://doi.org/10.1007/s00170-023-10813-7

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