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

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Research units

  • Imerys LtD


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.


Original languageEnglish
Title of host publication 4th Conference on Control and Fault Tolerant Systems (SysTol)
Publication statusPublished - 14 Oct 2019
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Control and Fault-Tolerant Systems - Casabalanca, Morocco
Duration: 18 Sep 201920 Sep 2019
Conference number: 4

Publication series

NameConference on Control and Fault Tolerant Systems (SysTol)
ISSN (Print)2162-1195
ISSN (Electronic)2162-1209


ConferenceInternational Conference on Control and Fault-Tolerant Systems
Abbreviated titleSysTol
Internet address

    Research areas

  • Mineral processing, Quality control, Multiple hearth furnace (MHF), Soft sensor, Advanced Control

ID: 39373875