Please use this identifier to cite or link to this item:
Title: A framework for integrating diagnostic knowledge with nonlinear optimization for data reconciliation and parameter estimation in dynamic systems
Authors: VACHHANI, P
Keywords: Process Flow-Rates
Decomposition Algorithms
Matrix Projection
Online Estimation
Neural Networks
Issue Date: 2001
Citation: CHEMICAL ENGINEERING SCIENCE, 56(6), 2133-2148
Abstract: Dynamic data reconciliation and parameter estimation are challenging problems for large, nonlinear process systems due to problem size and complexity, and the effects of nonlinearities. Recently, an elegant nonlinear optimization formulation has been proposed in the literature. In this work, we extend the nonlinear reconciliation problem to include the detection of the biased parameters. The central idea in this framework is the recognition that the biased parameter identification problem can be viewed as a diagnostic problem, and methods from fault diagnosis literature may be brought in to improve the performance. Once the biased parameter is identified, then the estimation of the bias is performed using nonlinear optimization methods. Using several case studies, this framework is shown to both, detect and produce acceptable estimates of the biased parameters. Since, the bias detection and estimation are decoupled, this framework is shown to provide faster and more accurate estimates for real-time applications. (C) 2001 . .
ISSN: 0009-2509
Appears in Collections:Article

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.