Control Strategy of a Multiple Hearth Furnace Enhanced by Machine Learning Algorithms

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Conference article in proceedings
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Date
2019-10-14
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Mcode
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Language
en
Pages
250-256
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Abstract
An enhanced control strategy for a multiple hearth furnace for the purpose of kaolin production is developed and presented in this paper. Mineralogy-driven machine learning algorithms play a key role in the optimization strategy of the furnace. First, the capacity and temperature setpoints for furnace control are determined based on the feed ore mineralogy. Next, the capacity is optimized by combining the prediction of soluble alumina content and mullite content, while maintaining the quality of the product. The stabilizing control level compensates the disturbances with a feedforward control, which uses a spinel phase reaction rate soft sensor, aimed at minimizing the energy use of the furnace. The control concept is successfully tested by simulation using industrial data. Finally, a sampling campaign and software testing of the soft sensors and machine learning algorithms are performed at the industrial site. The results are presented and discussed in the paper.
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Keywords
Mineral processing, Quality control, Multiple hearth furnace (MHF), Soft sensor, Advanced Control
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Citation
Gomez Fuentes , J , Jämsä-Jounela , S-L , Moseley , D & Skuse , T 2019 , Control Strategy of a Multiple Hearth Furnace Enhanced by Machine Learning Algorithms . in 4th Conference on Control and Fault Tolerant Systems (SysTol) . Conference on Control and Fault Tolerant Systems (SysTol) , IEEE , pp. 250-256 , International Conference on Control and Fault-Tolerant Systems , Casabalanca , Morocco , 18/09/2019 . https://doi.org/10.1109/SYSTOL.2019.8864797