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The International Journal of Advanced Manufacturing Technology

Erschienen in:

02.01.2024 | ORIGINAL ARTICLE

Development of a kiln petcoke mill predictive model based on a multi-regression XGBoost algorithm

verfasst von: Mohammed Toum Benchekroun, Smail Zaki, Mohamed Aboussaleh, Hajar Belrhiti, Fatoumata Diassana

Erschienen in: The International Journal of Advanced Manufacturing Technology | Ausgabe 7-8/2024

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Abstract

This paper presents an investigation into the optimization of petroleum coke mill or petcoke mill processes, to improve efficiency and reduce waste in the heavy industry within the cement plant where our study is conducted. Our mission was to create a robust algorithm that could properly anticipate the mill’s performance and improve its operations. To accomplish this, we started by performing a comprehensive data analysis. Next, we built numerous regression models, and then assessed the effectiveness of each model using four crucial metrics. The suggested model is a multi-regression XGBoost (eXtreme gradient boosting) model, performing with a 90% score. Finally, the model will then be used to build an algorithm that can optimize the input values to accomplish the intended results.

Vorheriger Artikel Microstructure, hardness, and wear resistance at room and high temperature of Stellite-6/WC-6Co coatings deposited by laser cladding process

Nächster Artikel Systematic approach to improve overlap laser welding of AA5052-H32 with dissimilar thickness by evaluation of mechanical performance, undercut, and welding penetration

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Titel: Development of a kiln petcoke mill predictive model based on a multi-regression XGBoost algorithm
verfasst von: Mohammed Toum Benchekroun
Smail Zaki
Mohamed Aboussaleh
Hajar Belrhiti
Fatoumata Diassana
Publikationsdatum: 02.01.2024
Verlag: Springer London
Erschienen in: The International Journal of Advanced Manufacturing Technology / Ausgabe 7-8/2024
Print ISSN: 0268-3768
Elektronische ISSN: 1433-3015
DOI: https://doi.org/10.1007/s00170-023-12689-z

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