Paper Info
Title
Neural Modeling and Control of a 13C Isotope Separation Process.
Abstract
The paper presents a solution for the 13C isotope concentration control inside and at the output of a separation column, solution based on the Internal Model Control strategy. The 13C isotope results from a chemical exchange process carbon dioxide – carbamate, which is a distributed parameter process. In order to model the mentioned process, an original form of the approximating analytical solution which describes the process work in transitory regime is determined. The evolution of the approximating solution depends both on time and on the position from the column height. The reference model of the fixed part of the control structure is implemented using neural networks, representing an original solution due to the fact that a neural model is determined for a distributed parameter process. The controller is, also, implemented using neural networks, its main parameter being adapted in relation to the transducer position change in the separation column. The advantages of using the proposed concentration control strategy consist of: the possibility of controlling the value of the 13C isotope concentration in any point from the separation column height; the improvement of the system performance regarding the settling time; the possibility to reject the effect of the disturbances.
Year
DOI
Venue
2015
10.5220/0005549002540263
ICINCO
Keywords
Field
DocType
Separation Column,13C Isotope,Internal Model Control Strategy,Neural Networks,Distributed Parameter Process,Approximating Analytical Solution
Isotope separation,Transducer,Control theory,Reference model,Control theory,Settling time,Control engineering,Engineering,Artificial neural network,Internal model
Conference
Volume
Citations 
PageRank 
01
1
0.43
References 
Authors
0
8
Name
Order
Citations
PageRank
Vlad Muresan169.31
Mihail Abrudean22411.47
Honoriu Valean3178.28
Tiberiu Colosi475.72
mihaelaligia unguresan513.48
Valentin Sita612.46
iulia clitan714.83
Daniel Moga812.80