Theses and Dissertations

Fall 2011

Qi, Fei

The large number of control loops in a modern industrial plant poses a serious challenge for operators and engineers to monitor these loops to maintain them at optimal conditions continuously. Much research has been done on control loop performance assessment and monitoring of individual components

missing pattern concept is introduced. The incomplete evidence problems are categorized into single missing pattern ones and multiple missing pattern ones. A novel method based on marginalization over an underlying complete evidence matrix (UCEM) is proposed to include the incomplete evidences into the

under the Bayesian framework. An approach to estimate the distributions of monitor readings with sparse historical samples is proposed to alleviate the intensive requirement of historical data. The statistical distribution functions for several monitoring algorithm outputs are analytically derived. A

Fall 2018

Li, Ruoxia

develop an estimator for the state PDF of arbitrary distribution. In this work, we develop an estimator based on a Gaussian mixture model (GMM) coupled with the ensemble Kalman filter (EnKF) specifically for estimation with multimodal state distributions.The second problem is that the conventional

a Gaussian distribution. This presents a challenge for Kalman-based state estimators such as the extended Kalman filter, since they model the state PDF as Gaussian. In order to achieve more accurate estimation, the modeling of the state distribution needs to be improved. The first problem is to

work, we develop a novel state estimation technique to incorporate inequality constraints for the case of Gaussian filters. Furthermore, we consider the constrained estimation for the case where the state PDF cannot be approximated with a Gaussian distribution. To this end, we develop a framework to

Spring 2017

Shen, Wenhan

and parameter uncertainties. Robust optimization (RO) approximation, a novel method dealing with joint chance constraints, is investigated to solve CCMPC problem. This method leads to results close to the true optimal and is not restricted to certain types of distribution. This work is further applied

on the steam assisted gravity drainage (SAGD) process. Constraint violations are greatly reduced by using the RO method. For system noises, the RO method can be directly applied with the inclusion of uncertainty sets. The type of uncertainty set is selected based on the distribution. Two-layer

optimization is proposed, one layer guarantees probability satisfaction and the other layer deals with optimizing the cost. Compared with traditional analytical methods, RO method is not limited to specific distribution and shows better performance in objective function. For parameter uncertainties, random

Fall 2020

Fan, Lei

features can be extracted and the impact of outliers is alleviated by the latent variance scale. The next contribution of this thesis is to develop a semi-supervised model based on probability slow feature analysis to include the information from quality variables in the extracted latent features while

information of the process. With a probabilistic formulation, dynamic latent variable models, based on extracting slowly varying features, are developed in this thesis to address the aforementioned data irregularities, thus give reliable prediction results of quality variables that are otherwise difficult to

-distribution that has heavier tails, more weights can be assigned to the outliers thus they can be properly accounted for during modeling process. In feature extraction phase, a weighted Kalman gain is proposed since it violates the Gaussian assumption of the traditional Kalman filter. Smoother and slower