Simplified Neutrosophic Petri Nets Used for Identification of Superheat Degree

As an important modeling tool for knowledge representation and reasoning (KRR), fuzzy Petri nets (FPNs) have been applied in kinds of fields. However, there exist some deficiencies for existing FPNs, resulting in application limitations. (1) The sum of membership and non-membership is less than 1, which is hard to model various types of uncertain knowledge by existing FPNs; (2) The reasonable and nonidentical experiential knowledge for electrolysis operation among domain experts should be fused; (3) The existing knowledge reasoning algorithm min, max, and product operators may not work well in many practical applications. In an effort to overcome the shortcomings of existing FPNs, a new type of FPNs is proposed, called simplified neutrosophic Petri nets (SNPNs). First, the simplified neutrosophic sets (SNSs) are introduced into SNPNs, characterized by three independent degrees of truth-membership, indeterminacy-membership and falsity-membership, to depict the experiential cognition of domain experts. Second, the extended TOPSIS (ETOPSIS) is proposed for knowledge fusion. Third, hybrid averaging SNNs concurrent reasoning algorithm (HASCRA) is also proposed to improve the knowledge reasoning efficiency. Finally, a novel model for the identification of superheat degree of aluminum electrolysis cell (ISDAEC) is proposed based on SNPNs. Comparative experimental results show that the SNPNs provide a feasible and practical decision-making method. Moreover, this fact denotes that the proposed method has potential applications in intelligent ISDAEC.