Reinforcement Learning-Based Supervisory Control Strategy for a Rotary Kiln Process

Abstract

Rotary kiln is a kind of large scale sintering device widely used in metallurgical, cement, refractory materials, chemical and environment protection industries. Its complicated working mechanism includes physical change and chemical reaction of material, procedure of combustion, thermal transmission among gaseous fluid, solid material fluid and the liner. The automation problem of such processes remains unsolved because of the following inherent complexities. A rotary kiln is a typical distributed parameter system with correlative temperature distribution of gaseous phase and solid phase along its axis direction. Limited by device rotation and technical design, sensors and actuators can be installed only at the kiln head and kiln tail, and lumped parameter control strategies are employed to deal with distributed parameter problems. Thus the rotary kiln process is a multivariable nonlinear system with strong coupling, large lag and uncertain disturbances. Moreover, the key controlled variable of burning zone temperature is measured with serious disturbances. Most of rotary kilns are still under manual control with human operator observing the burning status. As a result, the product quality is hard to be kept consistent and energy consumption remains high, kiln liner is easy to wear out, the kiln running rate and yield is low. Although several advanced control strategies including fuzzy control (Holmblad & Ostergaard, 1995) , intelligent control (Jarvensivu et al., 2001a; Jarvensivu et al., 2001b) and predictive control (Zanovello & Budman, 1999) have been introduced into process control of rotary kiln, all these researches focused on stabilizing some key controlled variables but are valid only for cases that boundary conditions do not change frequently. As a matter of fact, the boundary conditions of a rotary kiln often change. For example, the material load, water content and components of the raw material slurry vary frequently and severely. Moreover, the offline analysis data of components of raw material slurry reach the operator with large time delay. Thus conventional control strategy cannot reach automatic control and keep the product quality consistent. To deal with the complexity of operation conditions, the authors have proposed an intelligent control system based on human-machine interaction for an alumina rotary kiln in (Zhou et al., 2004; Zhou et al., 2006), in which human intervention function was design so that, if the operation condition changed largely, the human operator observing burning status can intervene the control actions when the system is in the automatic control mode to enhance the adaptability of the control system. Source: Reinforcement Learning: Theory and Applications, Book edited by Cornelius Weber, Mark Elshaw and Norbert Michael Mayer ISBN 978-3-902613-14-1, pp.424, January 2008, I-Tech Education and Publishing, Vienna, Austria O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m