Advanced Predictive Process Control for Industrial Thickeners
Mouna El Hamrani, Mouna El Hamrani, Khalid Benjelloun, Khalid Benjelloun, Jean-Pierre Kenné, Saad Maarouf, Mohamed Elkhouakhi
2025
Abstract
Efficient control of industrial thickeners is crucial for optimizing solid-liquid separation processes, especially in fields like mining and wastewater treatment. Traditional model predictive control (MPC) strategies, even though useful in most applications, can face trouble trying to maintain their performance when faced with time-varying dynamics due to factors such as wear and tear of equipment or changes in feed properties. To address these limitations, this paper highlights an adaptive model predictive control (AMPC) strategy that uses real-time parameter identification to update the prediction model of the usual MPC algorithm. The results show that while AMPC improves the robustness of the controller significantly, keeping critical process parameters such as slurry density well within operational limits under changing conditions, it still faces a number of challenges. AMPC struggles to compensate for unknown disturbances or to optimize flocculant consumption, resulting in economic problems. These results suggest that, despite the improvements offered by AMPC, further research is required to develop advanced disturbance rejection mechanisms and incorporate flocculant optimization strategies for more efficient and cost-effective performances.
DownloadPaper Citation
in Harvard Style
El Hamrani M., Benjelloun K., Kenné J., Maarouf S. and Elkhouakhi M. (2025). Advanced Predictive Process Control for Industrial Thickeners. In Proceedings of the 15th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH; ISBN 978-989-758-759-7, SciTePress, pages 262-269. DOI: 10.5220/0013508000003970
in Bibtex Style
@conference{simultech25,
author={Mouna El Hamrani and Khalid Benjelloun and Jean-Pierre Kenné and Saad Maarouf and Mohamed Elkhouakhi},
title={Advanced Predictive Process Control for Industrial Thickeners},
booktitle={Proceedings of the 15th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH},
year={2025},
pages={262-269},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013508000003970},
isbn={978-989-758-759-7},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 15th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH
TI - Advanced Predictive Process Control for Industrial Thickeners
SN - 978-989-758-759-7
AU - El Hamrani M.
AU - Benjelloun K.
AU - Kenné J.
AU - Maarouf S.
AU - Elkhouakhi M.
PY - 2025
SP - 262
EP - 269
DO - 10.5220/0013508000003970
PB - SciTePress