Fernando V. Lima
B.S. Ch.E., University of Sao Paulo, Brazil, 2003
Ph.D., Tufts University, 2007
The use of linearized models to represent a nonlinear chemical process is a common practice in process identification and control. However, these models may not accurately represent the dynamics of the real process. The objective of this research is to identify fully nonlinear, stochastic models combining first principles and process operating data. These models will be primarily used to improve the operations of nonlinear Model Predictive Controllers (MPC).
This work focuses on the critical evaluation and implementation of nonlinear state estimators to chemical processes. The studied estimators include: Moving Horizon Estimators (MHE), Particle Filters (PF), Unscented Kalman Filters (UKF) and Extended Kalman Filters (EKF).
This research has two sub-topics:
Data-based covariance estimation: application of the linear (Rajamani and Rawlings, 2008) and nonlinear (Rajamani et. al., 2007) Autocovariance Least-Squares (ALS) techniques to determine noise covariances from routine operating data. These covariances are used to tune state estimators such as Kalman Filter (KF), Extended Kalman Filter (EKF) and Moving Horizon Estimators (MHE).
Data-based disturbance structure estimation: estimation of the stochastic disturbance structure that affects the states. This structure provides information about the minimum number of independent disturbances entering the data.
The examples of application and their operating data are provided by industrial partners from the TWCCC consortium.
|Personal Web Page: jbrwww.che.wisc.edu/~lima|
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University of Wisconsin
Department of Chemical Engineering
Madison WI 53706