Abstract
In this study, we explore the effectiveness of a hybrid modelling approach that seamlessly integrates data-driven techniques, specifically Machine Learning (ML), with physics-based equations in Simulation. In cases where real-world data for industrial processes is insufficient, a simulation tool is employed to generate an extensive dataset of process variables under varying operating conditions. Subsequently, this dataset is utilized for training the Machine Learning model. The paper showcases a practical use case of this hybrid modelling approach, revealing a model that consistently demonstrates strong predictive accuracy and reliability within the specific industrial context we investigate. By merging the insights derived from physics-based understanding with the adaptability of data-driven Machine Learning, the hybrid model offers a comprehensive solution for precise and accurate predictions.
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Acknowledgements
This work is supported by the Ministry of Higher Education, Scientific Research and Innovation, the Digital Development Agency (DDA) and the CNRST of Morocco through Al-Khawarizmi program. This publication is part of the work undertaken by the consortium of partners which is composed of MAScIR (Moroccan Foundation for Advanced Science, Innovation and Research), Reminex; the R &D and Engineering subsidiary of Managem group, UCA, ENSIAS and ENSMR. We would like to thank the Managem Group and its subsidiary CMG for allowing the conduction of this research on its operational site as an industrial partner of this project.
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Hasidi, O. et al. (2024). Data-Driven and Model-Driven Approaches in Predictive Modelling for Operational Efficiency: Mining Industry Use Case. In: Mosbah, M., Kechadi, T., Bellatreche, L., Gargouri, F. (eds) Model and Data Engineering. MEDI 2023. Lecture Notes in Computer Science, vol 14396. Springer, Cham. https://doi.org/10.1007/978-3-031-49333-1_9
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DOI: https://doi.org/10.1007/978-3-031-49333-1_9
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