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

• Published in: Information sciences Vol. 529; pp. 141 - 165
• Main Authors: Yue, Weichao, Gui, Weihua, Xie, Yongfang
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.08.2020
• Subjects: Aluminum electrolysis cell condition identification; Cognitive inconsistency; Extended TOPSIS; Interval type-2 fuzzy sets; Knowledge representation and reasoning; Linguistic Petri nets; Extended TOPSIS; Interval type-2 fuzzy sets; Knowledge representation and reasoning; Aluminum electrolysis cell condition identification; Cognitive inconsistency; Linguistic Petri nets
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2020.03.079

•We propose the idea and framework of the experiential knowledge representation and reasoning method and application for the identification of aluminum electrolysis cell condition.•The extended TOPSIS is proposed and used to determine the value of linguistic term.•Linguistic Petri nets, used for KRR, is proposed combining with interval type-2 fuzzy sets, extended TOPSIS and fuzzy Petri nets.•The experiential knowledge representation and reasoning model for aluminum electrolysis cell condition identification is proposed.•The proposed method is promising to replace artificial decision making and realize the intelligent identification of aluminum electrolysis cell condition. 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.