Modelling of gasoline blending via discrete-time neural networks

Wen Yu; Marco A. Moreno-Armendariz; E. Gómez-Ramírez

doi:10.1109/IJCNN.2004.1380130

Modelling of gasoline blending via discrete-time neural networks

Wen Yu, Marco A. Moreno-Armendariz, E. Gómez-Ramírez

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Gasoline blending is an important operation in chemical industry. A good model for the blending process is beneficial for supervision operation, prediction of gasoline qualities and realizing model-based optimal control. Gasoline blending process includes static and dynamic properties which are corresponded to thermodynamic and the storage tank respectively. Since the blending does not follow the ideal mixing rule in practice, we propose static and dynamic neural networks to approximate the blending process. Input-to-state stability approach is applied to access new robust learning algorithms of the neural networks. Numerical simulations are provided to illustrate the neuro modeling approaches.

Original language	English
Title of host publication	2004 IEEE International Joint Conference on Neural Networks - Proceedings
Pages	1291-1296
Number of pages	6
DOIs	https://doi.org/10.1109/IJCNN.2004.1380130
State	Published - 2004
Externally published	Yes
Event	2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary Duration: 25 Jul 2004 → 29 Jul 2004

Publication series

Name	IEEE International Conference on Neural Networks - Conference Proceedings
Volume	2
ISSN (Print)	1098-7576

Conference

Conference	2004 IEEE International Joint Conference on Neural Networks - Proceedings
Country/Territory	Hungary
City	Budapest
Period	25/07/04 → 29/07/04

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10.1109/IJCNN.2004.1380130

Cite this

@inproceedings{1fbfca4b378341518e5893ea32e15303,

title = "Modelling of gasoline blending via discrete-time neural networks",

abstract = "Gasoline blending is an important operation in chemical industry. A good model for the blending process is beneficial for supervision operation, prediction of gasoline qualities and realizing model-based optimal control. Gasoline blending process includes static and dynamic properties which are corresponded to thermodynamic and the storage tank respectively. Since the blending does not follow the ideal mixing rule in practice, we propose static and dynamic neural networks to approximate the blending process. Input-to-state stability approach is applied to access new robust learning algorithms of the neural networks. Numerical simulations are provided to illustrate the neuro modeling approaches.",

author = "Wen Yu and Moreno-Armendariz, {Marco A.} and E. G{\'o}mez-Ram{\'i}rez",

year = "2004",

doi = "10.1109/IJCNN.2004.1380130",

language = "Ingl{\'e}s",

isbn = "0780383591",

series = "IEEE International Conference on Neural Networks - Conference Proceedings",

pages = "1291--1296",

booktitle = "2004 IEEE International Joint Conference on Neural Networks - Proceedings",

note = "2004 IEEE International Joint Conference on Neural Networks - Proceedings ; Conference date: 25-07-2004 Through 29-07-2004",

}

Yu, W, Moreno-Armendariz, MA & Gómez-Ramírez, E 2004, Modelling of gasoline blending via discrete-time neural networks. in 2004 IEEE International Joint Conference on Neural Networks - Proceedings. IEEE International Conference on Neural Networks - Conference Proceedings, vol. 2, pp. 1291-1296, 2004 IEEE International Joint Conference on Neural Networks - Proceedings, Budapest, Hungary, 25/07/04. https://doi.org/10.1109/IJCNN.2004.1380130

Modelling of gasoline blending via discrete-time neural networks. / Yu, Wen; Moreno-Armendariz, Marco A.; Gómez-Ramírez, E.
2004 IEEE International Joint Conference on Neural Networks - Proceedings. 2004. p. 1291-1296 (IEEE International Conference on Neural Networks - Conference Proceedings; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Yu, Wen

AU - Moreno-Armendariz, Marco A.

AU - Gómez-Ramírez, E.

PY - 2004

Y1 - 2004

N2 - Gasoline blending is an important operation in chemical industry. A good model for the blending process is beneficial for supervision operation, prediction of gasoline qualities and realizing model-based optimal control. Gasoline blending process includes static and dynamic properties which are corresponded to thermodynamic and the storage tank respectively. Since the blending does not follow the ideal mixing rule in practice, we propose static and dynamic neural networks to approximate the blending process. Input-to-state stability approach is applied to access new robust learning algorithms of the neural networks. Numerical simulations are provided to illustrate the neuro modeling approaches.

AB - Gasoline blending is an important operation in chemical industry. A good model for the blending process is beneficial for supervision operation, prediction of gasoline qualities and realizing model-based optimal control. Gasoline blending process includes static and dynamic properties which are corresponded to thermodynamic and the storage tank respectively. Since the blending does not follow the ideal mixing rule in practice, we propose static and dynamic neural networks to approximate the blending process. Input-to-state stability approach is applied to access new robust learning algorithms of the neural networks. Numerical simulations are provided to illustrate the neuro modeling approaches.

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DO - 10.1109/IJCNN.2004.1380130

M3 - Contribución a la conferencia

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SN - 0780383591

T3 - IEEE International Conference on Neural Networks - Conference Proceedings

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BT - 2004 IEEE International Joint Conference on Neural Networks - Proceedings

T2 - 2004 IEEE International Joint Conference on Neural Networks - Proceedings

Y2 - 25 July 2004 through 29 July 2004

ER -

Modelling of gasoline blending via discrete-time neural networks

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