Next generation controllers for kiln/cooler and mill applications based on model predictive control and neural networks

摘要

Model predictive control (MPC) has become the standard supervisory control tool in some process industries, including oil refining and petrochemicals. It has been introduced into the cement industry, in a kiln/cooler application at Pretoria Portland Cement's (PPC) Dwaalboom plant in South Africa. This application differs from the well-established expert system approach in that it incorporates a model of the process rather than a model of the operator. The continuous regulation and disturbance rejection of MPC is well suited to kiln/cooler control, and for example the application recovers from major upsets such as coating drop three times faster than typical operator intervention. Mills have been known to demonstrate severe nonlinear behavior, and linear controllers in mill applications have yielded only varying degrees of success. Most applications are eventually turned off due to poor performance caused by this nonlinear behaviour. Nonlinear MPC has been applied to the cement mill at Dwaalboom-a closed circuit ball mill. Gains are calculated at each control execution using a neural network model built from three months of log sheet data. Gains in the controller change by as much as a factor of fifteen. This controller has demonstrated significantly improved setpoint tracking and disturbance rejection over all three-product grades.