Supervisory Control for Rotary Kiln Temperature Based on Reinforcement Learning

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.