Knowledge based process control supervision and diagnosis: the AEROLID approach

Abstract

The artificial intelligence incidence in process control, although an active area in the researchers community and even with some implementations at industrial environment, is not sufficiently evaluated in numerical terms for the long term. The present article shows such an evaluation of a knowledge based system, developing supervisory control tasks in the sugar production from sugar-beet, and paying particular attention to fault detection and diagnosis. A way of conceiving supervision for continuous processes is presented and supported with this industrial application. The expert system carrying out supervisory tasks operates in a VAX ® workstation, directly over the distributed control system. The expert system development tool is G2 ® which has real-time facilities. Although the core system was developed in G2, it also consists of some external modules because it combines both analytical and artificial intelligence problem resolution techniques. The global architecture, as well as the implementation details of the modules necessary for fault identification, are presented altogether with the experimental results obtained from the factory field.