STATISTICAL METHODS FOR PROCESS MONITORING AND CONTROL

Abstract

Nowadays, large-scale datasets are generated in industrial processes as varieties of digital instruments, analytical sensors and data devices are utilized. The data does not transfer to useful knowledge automatically. In the current age of big data, it is critically important to develop data-driven techniques to harness industrial data to make better decisions. Statistical methods can help to make sense of the variety of data from industrial processes. Specifically, this thesis addresses three applications of statistical methods in process engineering in order to obtain different kinds of process knowledge. With the high-dimensional and correlated process data, multivariate statistical process monitoring methods have been developed to extract useful information from a large amount of process data and detect various types of process faults. Specifically, an independent component analysis (ICA) mixture model based local dissimilarity method is developed for performance monitoring of multimode dynamic processes with non-Gaussian features in each operating mode. Then, two video analysis based pellet sizing methods are proposed for measuring the pellet size distributions without any off-line and intrusive tests. The videos of free-falling pellets are first taken and then the free-falling tracks of pellets in video frames are analyzed through the two video analysis based pellet sizing approaches. The utility of these two video analysis based pellet sizing methods is demonstrated through the online measurement and estimation of free-falling nickel pellets in two test videos. Moreover, a subspace projection based model-plant mismatch detection and isolation method is developed for the closed-loop MPC systems within state-space framework. The model quality indices are developed through subspace projection in order to eliminate the effects of system feedback. The paper machine headbox process with MIMO MPC controller is used to demonstrate the effectiveness of the proposed approach in detecting and isolating different kinds of model-plant mismatches.

Nowadays, large-scale datasets are generated in industrial processes as varieties of digital instruments, analytical sensors and data devices are utilized. The data does not transfer to useful knowledge automatically. In the current age of big data, it is critically important to develop data-driven techniques to harness industrial data to make better decisions. Statistical methods can help to make sense of the variety of data from industrial processes. Specifically, this thesis addresses three applications of statistical methods in process engineering in order to obtain different kinds of process knowledge. With the high-dimensional and correlated process data, multivariate statistical process monitoring methods have been developed to extract useful information from a large amount of process data and detect various types of process faults. Specifically, an independent component analysis (ICA) mixture model based local dissimilarity method is developed for performance monitoring of multimode dynamic processes with non-Gaussian features in each operating mode. Then, two video analysis based pellet sizing methods are proposed for measuring the pellet size distributions without any off-line and intrusive tests. The videos of free-falling pellets are first taken and then the free-falling tracks of pellets in video frames are analyzed through the two video analysis based pellet sizing approaches. The utility of these two video analysis based pellet sizing methods is demonstrated through the online measurement and estimation of free-falling nickel pellets in two test videos. Moreover, a subspace projection based model-plant mismatch detection and isolation method is developed for the closed-loop MPC systems within state-space framework. The model quality indices are developed through subspace projection in order to eliminate the effects of system feedback. The paper machine headbox process with MIMO MPC controller is used to demonstrate the effectiveness of the proposed approach in detecting and isolating different kinds of model-plant mismatches.