Nonlinear dynamic data reconciliation and bias estimation of process measurements in an adiabatic stirred-tank reactor

Nonlinear dynamic data reconciliation and bias estimation of process measurements in an adiabatic stirred-tank reactor

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When process data is taken from the sensors of a plant, errors of varying degrees are inherent. Measured variables will most likely violate dynamic process models. Because of this, large volumes of data may be unreliable for process control, monitoring, and optimization. This paper describes a new m...

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Bibliographic Details
Published in:Philippine Engineering Journal 37, 2 (2016(D)).
Main Author: Pilario, Karl Ezra S.
Other Authors: Munoz, Jose Co
Format: Article
Language:English
Subjects:
Data reconciliation.
Bias estimation.
Adiabatic.
Stirred tank reactor.
Error detection.
Online Access:Also available online for University of the Philippines Diliman. Click here