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Optimization of neural network model structures for valve stiction modeling

H., Zabiri and N., Mazuki (2009) Optimization of neural network model structures for valve stiction modeling. In: 2009 International Conference on Signal Acquisition and Processing, 23 April 2009 through 5 April 2009;, Kuala Lumpur.

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Abstract

Stiction is the most commonly found valve problem in the process industry. Valve stiction may cause oscillations in control loops which increases variability in product quality, accelerates equipment wear and tear, or leads to system instability. To help understand and study the behavior of sticky valve, several valve stiction models have been proposed in the literature. In this paper, a black box Neural Network-based modeling approach is proposed to model valve stiction. It is shown that with optimum model structures, performance of the developed NN stiction model is comparable to other established method. © 2009 IEEE.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Component; Control valve stiction, neural network, modeling Component; Control valve stiction, neural network, modeling
Subjects:T Technology > TP Chemical technology
Departments / MOR / COE:Departments > Chemical Engineering
ID Code:3733
Deposited By: Haslinda Zabiri
Deposited On:28 Dec 2010 07:02
Last Modified:19 Jan 2017 08:25

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