Constrained Nonlinear Estimation for Industrial Process Fouling

Keywords

plugging, fouling, moving horizon estimation, industrial process control

Abstract

Industrial process monitoring tools require robust and efficient estimation techniques that maintain a high service factor by remaining online during abnormal operating conditions, such as during loss of measurements, changes in control status, or maintenance. Constraints incorporate additional process knowledge into estimation by bounding estimated disturbances within feasibility limits thereby providing robustness to faulty measurements or conditions that violate process models. Moving horizon estimation (MHE) and unscented Kalman filtering (UKF) are two estimation techniques that permit incorporation of constraints prior to evaluating the a priori estimate. This paper evaluates both constrained nonlinear estimators versus the extended Kalman filter (EKF) using industrial process data provided by ExxonMobil Chemical Company. Results provide short-term insight into the fouling process, and parameter estimates produced by UKF and MHE are shown to be more accurate than EKF.

Original Publication Citation

http://pubs.acs.org/doi/abs/10.1021/ie9018116

Document Type

Peer-Reviewed Article

Publication Date

2010-05-09

Permanent URL

http://hdl.lib.byu.edu/1877/3657

Publisher

Industrial & Engineering Chemistry Research

Language

English

College

Ira A. Fulton College of Engineering and Technology

Department

Chemical Engineering

University Standing at Time of Publication

Assistant Professor

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