Superheat Degree Recognition of Aluminum Electrolysis Cell Using Unbalance Double Hierarchy Hesitant Linguistic Petri Nets

Abstract

Superheat degree is a core technical parameter and management index of aluminum electrolysis cell. However, the exiting methods have limited abilities when applied to superheat degree recognition of aluminum electrolysis cell (SDRAEC). In addition, the important hesitant degree is ignored in the unbalance double hierarchy linguistic term set. To address these issues, an unbalance double hierarchy hesitant linguistic Petri net model and extended TOPSIS is proposed for SDRAEC. In this model, the coupling relationships among variables is made to be explicit knowledge, and the unbalance double hierarchy hesitant linguistic term set is proposed to represent the value of knowledge parameter. The relative entropy is introduced to enhance the performance of extended TOPSIS. Moreover, hybrid averaging unbalance double hierarchy hesitant linguistic term set concurrent reasoning algorithm is proposed to improve reasoning efficiency. Finally, thermal analysis experiments conducted in a real-world aluminum electrolysis plant are used to demonstrate the effectiveness of the proposed method. Compared with other methods, the accuracy of SDRAEC has been increased to 89.00%.