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

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

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Control Strategy of a Multiple Hearth Furnace Enhanced by Machine Learning Algorithms. / Gomez Fuentes, Jose; Jämsä-Jounela, Sirkka-Liisa; Moseley, David; Skuse, Tom.

4th Conference on Control and Fault Tolerant Systems (SysTol). IEEE, 2019. s. 250-256 (Conference on Control and Fault Tolerant Systems (SysTol)).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Harvard

Gomez Fuentes, J, Jämsä-Jounela, S-L, Moseley, D & Skuse, T 2019, Control Strategy of a Multiple Hearth Furnace Enhanced by Machine Learning Algorithms. julkaisussa 4th Conference on Control and Fault Tolerant Systems (SysTol). Conference on Control and Fault Tolerant Systems (SysTol), IEEE, Sivut 250-256, International Conference on Control and Fault-Tolerant Systems, Casabalanca, Marokko, 18/09/2019. https://doi.org/10.1109/SYSTOL.2019.8864797

APA

Gomez Fuentes, J., Jämsä-Jounela, S-L., Moseley, D., & Skuse, T. (2019). Control Strategy of a Multiple Hearth Furnace Enhanced by Machine Learning Algorithms. teoksessa 4th Conference on Control and Fault Tolerant Systems (SysTol) (Sivut 250-256). (Conference on Control and Fault Tolerant Systems (SysTol)). IEEE. https://doi.org/10.1109/SYSTOL.2019.8864797

Vancouver

Gomez Fuentes J, Jämsä-Jounela S-L, Moseley D, Skuse T. Control Strategy of a Multiple Hearth Furnace Enhanced by Machine Learning Algorithms. julkaisussa 4th Conference on Control and Fault Tolerant Systems (SysTol). IEEE. 2019. s. 250-256. (Conference on Control and Fault Tolerant Systems (SysTol)). https://doi.org/10.1109/SYSTOL.2019.8864797

Author

Gomez Fuentes, Jose ; Jämsä-Jounela, Sirkka-Liisa ; Moseley, David ; Skuse, Tom. / Control Strategy of a Multiple Hearth Furnace Enhanced by Machine Learning Algorithms. 4th Conference on Control and Fault Tolerant Systems (SysTol). IEEE, 2019. Sivut 250-256 (Conference on Control and Fault Tolerant Systems (SysTol)).

Bibtex - Lataa

@inproceedings{e6f58ffd3d47456393523f34288bff06,
title = "Control Strategy of a Multiple Hearth Furnace Enhanced by Machine Learning Algorithms",
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.",
keywords = "Mineral processing, Quality control, Multiple hearth furnace (MHF), Soft sensor, Advanced Control",
author = "{Gomez Fuentes}, Jose and Sirkka-Liisa J{\"a}ms{\"a}-Jounela and David Moseley and Tom Skuse",
year = "2019",
month = "10",
day = "14",
doi = "10.1109/SYSTOL.2019.8864797",
language = "English",
isbn = "978-1-7281-0381-5",
series = "Conference on Control and Fault Tolerant Systems (SysTol)",
publisher = "IEEE",
pages = "250--256",
booktitle = "4th Conference on Control and Fault Tolerant Systems (SysTol)",
address = "United States",

}

RIS - Lataa

TY - GEN

T1 - Control Strategy of a Multiple Hearth Furnace Enhanced by Machine Learning Algorithms

AU - Gomez Fuentes, Jose

AU - Jämsä-Jounela, Sirkka-Liisa

AU - Moseley, David

AU - Skuse, Tom

PY - 2019/10/14

Y1 - 2019/10/14

N2 - 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.

AB - 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.

KW - Mineral processing

KW - Quality control

KW - Multiple hearth furnace (MHF)

KW - Soft sensor

KW - Advanced Control

U2 - 10.1109/SYSTOL.2019.8864797

DO - 10.1109/SYSTOL.2019.8864797

M3 - Conference contribution

SN - 978-1-7281-0381-5

T3 - Conference on Control and Fault Tolerant Systems (SysTol)

SP - 250

EP - 256

BT - 4th Conference on Control and Fault Tolerant Systems (SysTol)

PB - IEEE

ER -

ID: 39373875