Fault detection and diagnosis of an industrial copper electrowinning process

Abstract

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 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 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.