Process monitoring of continuous processes with periodic operation patterns

Application of conventional statistical monitoring methods to periodic processes can result in frequent false alarms and/or missed faults due to their common non-stationary behavior seen over a period. To address this, we propose to identify and use a stochastic state-space model that describes statistical behavior of the changes occuring from period to period. This model, when retooled as a periodically time-varying model, can be used for on-line monitoring and estimation with the aid of a Kalman filter. The same model can also be used for inferential estimation of the variables that are difficult or slow to measure on-line. The proposed approach is applied to a simulation benchmark of waste-water treatment process, which exhibit strong diurnal changes in the feed stream, and compared against the Principal Component Analysis (PCA) and Partial Least Squares (PLS) methods.