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Fault detection and diagnosis of an industrial copper electrowinning process
Author(s) -
Wang Zhenheng,
Wiebe Susan,
Shang Helen
Publication year - 2016
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.22405
Subject(s) - electrowinning , fault detection and isolation , fault (geology) , process (computing) , computer science , copper , principal component analysis , reliability engineering , process engineering , engineering , metallurgy , materials science , artificial intelligence , chemistry , actuator , electrode , seismology , electrolyte , geology , operating system
Electrowinning is a key process in the production of high purity copper from copper sulphide minerals. Fault detection and diagnosis based on the industrial operating data would be desirable to avoid abnormal operation and ensure high product quality. In this paper, a fault detection and diagnosis algorithm using dynamic principal component analysis (DPCA) is applied to the industrial selenium/tellurium removal and copper electrowinning process. From the principal components obtained using DPCA, Hotelling T 2 and SPE are calculated to determine whether a fault has occurred. Tests on normal and faulty data sets confirm that the DPCA‐based fault detection method is effective for the industrial system. Contribution plots of variables to T 2 are obtained to determine the variables that contribute to the fault and to help locate the root causes of the fault. The DPCA‐based fault detection and diagnosis provide a convenient approach for enhancing the process operation and product quality for the industrial copper electrowinning system.