Hybrid neural network multivariable predictive controller for handling abnormal events in processing applications

We describe a hybrid controller that uses neural networks and multivariable predictive control (MPC) to handle abnormal events in process applications. The controller detects abnormal situations, such as grinding mill spills or mill power excursions in mineral processing, or incipient flooding in separation columns and then reconfigures the multivariable controller to stabilize the operations. Neural networks are typically used to detect and classify the abnormal situation and knowledge of process dynamics and interactions is used to reconfigure the multivariable predictive controller parameters to stabilize the operations. Thus the MPC can be configured and tuned to provide good control around the 'normal' operating range, and when an upset occurs and is detected a new set of tuning parameters are used.