Campus Units

Chemical and Biological Engineering, Industrial and Manufacturing Systems Engineering, Statistics

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

2005

Journal or Book Title

Chemical Engineering Research and Design

Volume

83

Issue

12

First Page

1391

Last Page

1398

DOI

10.1205/cherd.04270

Abstract

Two new approaches are presented for improved identification of measurement biases in linear pseudo steady-state processes. Both are designed to detect a change in the mean of a measured variable leading to an inference regarding the presence of a biased measurement. The first method is based on a likelihood ratio test for the presence of a mean shift. The second is based on a Bayesian decision rule (relying on prior distributions for unknown parameters) for the detection of a mean shift. The performance of these two methods is compared with that of a method given by Devanathan et al. (2000). For the process studied, both techniques were found to have higher identification power than the method of Devanathan et al. and appears to have excellent but sightly lower type I error performance than the Devanathan et al. method.

Comments

This is an accepted manuscript of an article published as Likelihood and Bayesian methods for accurate identification of measurement biases in pseudo steady-state processes. Chemical Engineering Research and Design: Part A, 2005, Vol. 83(A12), pp. 1391-1398. With Sriram Devanathan and Derrick Rollins. © 2005. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Copyright Owner

Institute of Chemical Engineers

Language

en

File Format

application/pdf

Published Version

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