APPLICATIONS OF MULTIVARIATE STATISTICAL-METHODS TO PROCESS MONITORING AND CONTROLLER-DESIGN

Novel ways of using multivariate statistical methods to develop proces s models for on-line monitoring and control are proposed. On a binary distillation column, PLS is used to develop a regression estimation us ing multiple tray temperature measurements and a manipulated variable to estimate and control distillate composition. Additionally, a feedba ck controller design based on a static PCA/PCR model is developed and demonstrated on the binary column. This controller's performance is co mpared with a PI controller for disturbance rejection and setpoint tra cking. On a real-world chemical process, it is shown how both PLS and PCS are necessary to model normal plant operations. These models permi t real-time monitoring and detection in a reduced subspace defined by the statistical independent variations in the data. Techniques for rea l-time monitoring and fault detection are demonstrated.