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SAG mill system diagnosis using multivariate process variable analysis
Author(s) -
Ko YoungDon,
Shang Helen
Publication year - 2011
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.20487
Subject(s) - mill , process (computing) , process engineering , principal component analysis , grinding , reliability engineering , multivariate statistics , computer science , variable (mathematics) , engineering , mathematics , artificial intelligence , machine learning , mechanical engineering , mathematical analysis , operating system
Semi‐autogenous grinding (SAG) of ore plays a critical role in a mineral processing plant. In SAG operations, abnormal conditions, such as overload or insufficient ore holdup, often result in inefficient production and unstable operation. It is, therefore, essential to monitor the process using effective technology so that abnormal or faulty conditions can be detected and addressed in a timely manner. In this study, investigation is focused on applying multivariate analysis in the monitoring and diagnosing of an industrial SAG operation. The results show that principal component analysis provides an effective methodology for on‐line monitoring and diagnosis. The detection and removal of faulty conditions will help to provide stable and cost‐efficient operation.