Abstract
The burning zone temperature in rotary kiln process is a vitally important controlled variable, on which the sinter quality mainly relies. Boundary conditions such as components of raw material slurry often change during kiln operation, but related offline analysis data delay to reach or even are unknown to the human operator. This causes unsatisfactory performance of the burning zone temperature controller and subsequent unstable production quality. To deal with this problem, a Q-learning-based supervisory control approach for burning zone temperature is proposed, in which the signals of human intervention are regarded as the reinforcement learning signals, so that the set point of burning zone temperature can be duly adjusted to adapt the fluctuations of the boundary conditions. This supervisory control system has been developed in DCS and successfully applied in an alumina rotary kiln. Satisfactory results have shown that the adaptability and performances of the control system have been improved effectively, and remarkable benefit has been obtained.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.