Experiential knowledge representation and reasoning based on linguistic Petri nets with application to aluminum electrolysis cell condition identification

Fuzzy Petri nets (FPNs) play an important role in knowledge representation and reasoning (KRR), and they have been widely used in many fields. Linguistic terms are usually used to express the experiential knowledge of decision-makers in the above fields. However, cognitive nonconformity, fuzziness and uncertainty of experiential knowledge are widespread in industrial production processes, making it difficult for current FPNs to precisely model the experience or cognition of experts. In an effort to overcome the shortcomings of FPNs, linguistic Petri nets (LPNs) are proposed based on interval type-2 fuzzy sets theory and FPNs in this paper. The extended TOPSIS (ETOPSIS) is proposed to fuse together the cognition of multiple decision-makers. An interval type-2 fuzzy ordered weighted averaging operator is proposed to improve the knowledge reasoning capability of LPNs. Two comparisons are presented to demonstrate the validity of the proposed ETOPSIS and LPNs. In addition, the KRR model for aluminum electrolysis cell condition identification (AECCI) is proposed and AECCI results show the proposed methods are efficient to embrace cognitive nonconformity and manage fuzziness and uncertainty of experiential knowledge. (C) 2020 Elsevier Inc. All rights reserved.