Process-wide performance monitoring

Today's process industries have shown a great concern on how to improve their product quality. The product quality, however, can be improved only when the process performance has been improved. Process performance monitoring, using only the routine operating data without interfering with the normal process operation, makes it possible to improve process performance and hence is useful to process industries. In this thesis, a hierarchical performance monitoring system is proposed and tested. The hierarchical monitoring system consists of two levels. The higher-level takes advantage of the advanced statistical regression analysis methods principal component analysis (PCA) and partial least squares (PLS) to assess large amounts of correlated process data. This level is able to provide overall process monitoring and a reliable detection for process abnormality. The lower-level is loop-oriented, and is designed to give detailed performance monitoring and fault diagnosis. It detects loop oscillation and locates the source of the oscillation; it detects high-friction in a valve and evaluates the controller itself. Spectral analysis and time series analysis methods and an adaptive nonlinear modeller (ANM) are used for the purpose of diagnosis at the lower-level. Considering the practical needs, a model-free controller tuning algorithm, iterative feedback tuning (IFT), is also built into the lower-level. Integrated in a complementary manner, the two levels can monitor the process performance with enhanced strength.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat