Modeling and Analyzing Multi-Factor Balanced Feedback System Based on Petri Net

Process control systems typically comprise multiple variables that impact system parameters, interacting and constraining each other with the objective of maintaining parameters within a specified range to ensure dynamic equilibrium. The reliability and safety of these systems are also of paramount concern. The multi-factor Balanced Feedback Net (BFN) represents one of the significant models for simulating systems with balanced feedback mechanisms. This paper refines and expands the definition of BFN, creating BFN models that incorporate varying quantities of balance factors. Based on this, we analyze the structural properties of BFN and provide relevant proofs. Given the complexity of BFN modeling, this paper introduces an innovative approach to translating real-world process control systems into BFN and develops an algorithm to assist users in automatically constructing BFN. For extreme situations that may arise in process control systems, we present an early recognition algorithm for extreme states in BFN. Theoretical proofs and case analyses complement each other, as demonstrated by the example of the water level control system of a steam boiler. This illustrates the effectiveness of methods and algorithms in complex system control and optimization.