James B. Rawlings Research Group


Fernando V. Lima
B.S. Ch.E., University of Sao Paulo, Brazil, 2003
Ph.D., Tufts University, 2007
fvlima[AT]wisc[DOT]edu

Current Work

State Estimation and Monitoring

Identification of Nonlinear and Stochastic Models

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).

State Estimation of Nonlinear Systems

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).

Identifying Disturbance Models from Data

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.

References:

1
Murali R. Rajamani and James B. Rawlings.
Estimation of the disturbance structure from data using semidefinite programming and optimal weighting.
Accepted for publication in Automatica, January, 2008.
2
Murali R. Rajamani, James B. Rawlings, and Tyler A. Soderstrom.
Application of a new data-based covariance estimation technique to a nonlinear industrial blending drum.
Submitted for publication in IEEE Transactions on Control Systems Technology, September, 2007.

Personal Web Page: jbrwww.che.wisc.edu/~lima

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University of Wisconsin
Department of Chemical Engineering
Madison WI 53706