Fault detection and isolation of acid gas removal units in a gas separation process using PLS

Partial least squares (PLS) is a potential data-driven technique to deal with a huge number of measured variables and complex relationship. It could be used for process monitoring even for chemical processes that have nonlinear dynamic properties. In this paper PLS is applied to detect two fault types which occurred in real Acid Gas Removal Units (AGRU) of a gas separation process. The developed PLS-based fault detection and isolation (FDI) is implemented under a careful selection of input variables and key quality variables for FDI purposes. The results indicate that the developed approach can predict and diagnose fault cases efficiently prior to process upset and shutdown.