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Conflict of Interest: In relation to this article, we declare that there is no conflict of interest.

Publication history: Received December 11, 2002
Accepted July 10, 2003

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Integrated Data Reconciliation with Generic Model Control for the Steel Pickling Process

Paisan Kittisupakorn^† Pornsiri Kaewpradit

Department of Chemical Engineering, Chulalongkorn University, Bangkok 10330, Thailand

paisan.k@chula.ac.th

Korean Journal of Chemical Engineering, November 2003, 20(6), 985-991(7), 10.1007/BF02706926

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Abstract

To implement an advanced control algorithm, measurements of process outputs are usually used to determine control action to a process. Nevertheless, measurements of process outputs are often subjected to measuring and signal errors as well as noise. Therefore, in this work, Generic Model Control (GMC), an advanced control technique, with data reconciliation technique has been applied to control the pH of the pickling process consisting of three pickling and three rinsing baths. Here, the data reconciliation problem involves six nodes and fourteen streams. The presence of errors in the data set is determined and identified via measurement test. In addition, the measurement error covariance is initially assumed to be a known variance matrix and is updated every iteration. Simulation results have shown that the reconciled process data give a better view of the true states of the process than raw measuring data. With these reconciled process data, the GMC controller can control the process at a desired set point with great success.

Keywords

Data Reconciliation Generic Model Control Steel Pickling Process

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