Identification and Control of an Industrial Thickener Using Historical Data


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Output type: Conference proceeding

UM6P affiliated Publication?: Yes

Author list: Oulhiq R., Benjelloun K., Kali Y., Saad M.

Editor list: Oulhiq R., Benjelloun K.,Kali Y.,Saad M.

Publisher: Institute of Electrical and Electronics Engineers Inc.

Publication year: 2021

Start page: 915

End page: 920

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107477432&doi=10.1109%2fSSD52085.2021.9429523&partnerID=40&md5=41d3b113d50fb7fb818742140bff8776

Languages: English (EN-GB)


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Abstract

In the mining industry, thickeners are used to increase density of slurries by removing water. When a thickener is in operation, the underflow slurry density should follow a given setpoint and stay inside a boundary layer. In general, thickeners are manually controlled. However, industrial experience has shown that manual control is not sufficient. For this reason, an advanced process control strategy is proposed in this paper. On the one hand, using historical data, a linear dynamic model of the studied thickener is developed. In this regard, data is collected, prepared, and data informativity is studied to ensure model identifiability and interpretability. On the other hand, a model predictive control is designed to control the process by manipulating the feed and the underflow slurry flowrates. The simulation results show that the method can be successfully used to control the underflow slurry density in the presence of feed variations and unmeasured disturbances. © 2021 IEEE.


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Last updated on 2021-26-11 at 23:16