Multiblock PLS-based localized process diagnosis

In this paper, we discuss a new fault detection and identification approach based on a multiblock partial least squares (MBPLS) method to monitor a complex chemical process and to model a key process quality variable simultaneously. In multivariate statistical process monitoring using MBPLS, four kinds of monitoring statistics are discussed. In particular, new definitions of the block and variable contributions to T2 and Q statistics are proposed and derived in order to identify faults. Also, the relative contribution, which is the ratio of the contribution to the corresponding upper control limit, is considered to find process variables or blocks responsible for faults. As an application study, a large wastewater treatment process in a steel mill plant is monitored and the effluent chemical oxygen demand, which indicates the current process performance, is modeled based on the proposed MBPLS-based fault detection and diagnosis method.