Towards timed fuzzy Petri net algorithms for chemical abnormality monitoring

One critical problem in the operations of chemical processes is the occurrence of abnormal events. Therefore, an effective process monitoring methodology that can help detect, diagnose and predict abnormal events becomes potentially very useful. For the purpose of knowledge representation of chemical abnormality, a specified type of timed fuzzy Petri net (tFPN) approach is explicitly introduced in this paper. The dominant feature of tFPN metrics can be recognized from the fact that a timing factor is assigned to each transition, as well as a degree of reliability is associated with each place, which allows accurately representing the dynamic nature of fuzzy knowledge pertaining to abnormal events. Following a procedure towards abnormal event monitoring, two efficient algorithms in terms of abnormality prognostication and diagnosis are exploited by means of reachability analysis of tFPN. The benefits of derived techniques and solutions are illustrated through a case study consisting in a polypropylene reactor.

Research highlights
► A type of timed fuzzy Petri net (tFPN) is introduced for knowledge representation of chemical abnormality.
► Enabling algorithms using tFPN metrics are presented for abnormal event monitoring.
► Case studies consisting in a polypropylene reactor are performed to exemplify the approaches.