A novel dynamic timed fuzzy Petri nets modeling method with applications to industrial processes

Abstract

Time fuzzy Petri nets (TFPNs) have been widely used to describe the transfer correlations among industrial process variables. However, the assignments of parameters associated with traditional TFPNs mostly rely on human expert knowledge. Additionally, traditional TFPNs have limited abilities to deal with dynamic time delays between correlated variables. In response to these problems, a dynamic timed fuzzy Petri nets (DTFPNs) modeling approach based on the dynamic time delay analysis (e-DTA) is proposed. Firstly, the basic structure of Petri nets is determined by taking advantage of process knowledge. Subsequently, as an improvement, a colored graph describing dynamic time delays between correlated variables is created using data mining techniques. A reachability analysis with temporal constraints is accordingly performed to track the system evolution dynamically. The proposed method is applied to a numerical case and a distillation column simulation, verifying the effectiveness of the contribution.