Intelligent process monitoring for paper machines

Paper machine control is a complex control environment. It consists of a large number of smaller control units, typically programmable logic controllers, that are integrated into an overall control architecture. Basis weight and formation control are typical of such systems. Errors in measurements due to sensor malfunctioning, or process states outside the basic assumptions for the control action lead to unwanted and/or poor supervisory response. Manual control often becomes the only way to return the machine to the desired state. In this paper, we report on the initial results of a research to apply the theories of ontological control and the hybrid fuzzy-Boolean finite state machine to paper machine basis weight control. The objective of the research is to provide an automated error detection and recovery method when control encounters an unexpected change in the process environment