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Two‐level multi‐block operating performance optimality assessment for plant‐wide processes
Author(s) -
Zou Xiaoyu,
Wang Fuli,
Chang Yuqing,
Zhao Luping,
Zheng Wei
Publication year - 2018
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.23159
Subject(s) - block (permutation group theory) , computer science , identification (biology) , process (computing) , probabilistic logic , mathematical optimization , matching (statistics) , set (abstract data type) , fuzzy logic , mathematics , statistics , artificial intelligence , botany , geometry , biology , programming language , operating system
A process operating performance optimality assessment (POPOA) consists of an optimal degree online assessment and non‐optimal cause identification, which contribute to maintaining a high comprehensive economic index (CEI) of the production. However, two main problems limit the application of the traditional POPOA methods, i.e., the plant‐wide process characteristics and the coexistence of both the quantitative and qualitative variables. To overcome the two problems for POPOA, a novel two‐level multi‐block assessment method based on the fuzzy probabilistic rough set (FPRS) is proposed in this research. The operating performance grade of both the global and sub‐block level are properly defined, where the sub‐block assessment indices, which are difficult to obtain, are not required. Different from traditional multi‐block methods due to the novel offline modelling method, an explicit global model is unnecessary. The global performance grade is directly determined by the sub‐block performance grades. When the process is operating at a non‐optimal performance grade, the responsible sub‐block can be rapidly identified through online assessment. The proposed non‐optimal cause identification technique is carried out in the non‐optimal sub‐blocks, based on a newly‐defined matching degree function. The identified non‐optimal causes also contribute to the actual production adjustment to obtain the optimal performance. Finally, the proposed POPOA method is successfully applied to a gold hydrometallurgy process, which is a typical plant‐wide process with hybrid types of variables.