Neural networks used for model predictive control of the fluid catalytic cracking unit

V. M. Cristea; L. Toma; S. P. Agachi

Neural networks used for model predictive control of the fluid catalytic cracking unit

V. M. Cristea, L. Toma, S. P. Agachi

Chemical, Materials & Metallurgical Engineering

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

1 Citation (Scopus)

Abstract

A statistical model using neural networks (NN) has been developed for an industrial FCC unit (FCCU) of Universal Oil Products type. The emerged NN model was used to implement FCCU control using nonlinear model predictive control (NMPC) algorithm. The control performance of the NMPC based on NN model was studied in the presence of representative disturbances. Both control performance requirements, setpoint tracking, and disturbance rejection, were fulfilled showing short settling time, reduced overshoot, and zero offset. This is an abstract of a paper presented at the 7th World Congress of Chemical Engineering (Glasgow, Scotland 7/10-14/2005).

Original language	English
Title of host publication	7th World Congress of Chemical Engineering, GLASGOW2005, incorporating the 5th European Congress of Chemical Engineering - Congress Manuscripts
Pages	55
Number of pages	1
Publication status	Published - 2005
Event	7th World Congress of Chemical Engineering, GLASGOW2005, incorporating the 5th European Congress of Chemical Engineering - Glasgow, Scotland, United Kingdom Duration: Jul 10 2005 → Jul 14 2005

Other

Other	7th World Congress of Chemical Engineering, GLASGOW2005, incorporating the 5th European Congress of Chemical Engineering
Country	United Kingdom
City	Glasgow, Scotland
Period	7/10/05 → 7/14/05

All Science Journal Classification (ASJC) codes

Energy(all)

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@inproceedings{7809bb08f8634ae9b2b5f85069c6a462,

title = "Neural networks used for model predictive control of the fluid catalytic cracking unit",

abstract = "A statistical model using neural networks (NN) has been developed for an industrial FCC unit (FCCU) of Universal Oil Products type. The emerged NN model was used to implement FCCU control using nonlinear model predictive control (NMPC) algorithm. The control performance of the NMPC based on NN model was studied in the presence of representative disturbances. Both control performance requirements, setpoint tracking, and disturbance rejection, were fulfilled showing short settling time, reduced overshoot, and zero offset. This is an abstract of a paper presented at the 7th World Congress of Chemical Engineering (Glasgow, Scotland 7/10-14/2005).",

author = "Cristea, {V. M.} and L. Toma and Agachi, {S. P.}",

year = "2005",

language = "English",

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booktitle = "7th World Congress of Chemical Engineering, GLASGOW2005, incorporating the 5th European Congress of Chemical Engineering - Congress Manuscripts",

note = "7th World Congress of Chemical Engineering, GLASGOW2005, incorporating the 5th European Congress of Chemical Engineering ; Conference date: 10-07-2005 Through 14-07-2005",

}

Cristea, VM, Toma, L & Agachi, SP 2005, Neural networks used for model predictive control of the fluid catalytic cracking unit. in 7th World Congress of Chemical Engineering, GLASGOW2005, incorporating the 5th European Congress of Chemical Engineering - Congress Manuscripts. pp. 55, 7th World Congress of Chemical Engineering, GLASGOW2005, incorporating the 5th European Congress of Chemical Engineering, Glasgow, Scotland, United Kingdom, 7/10/05.

Neural networks used for model predictive control of the fluid catalytic cracking unit. / Cristea, V. M.; Toma, L.; Agachi, S. P.

7th World Congress of Chemical Engineering, GLASGOW2005, incorporating the 5th European Congress of Chemical Engineering - Congress Manuscripts. 2005. p. 55.

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

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AU - Toma, L.

AU - Agachi, S. P.

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N2 - A statistical model using neural networks (NN) has been developed for an industrial FCC unit (FCCU) of Universal Oil Products type. The emerged NN model was used to implement FCCU control using nonlinear model predictive control (NMPC) algorithm. The control performance of the NMPC based on NN model was studied in the presence of representative disturbances. Both control performance requirements, setpoint tracking, and disturbance rejection, were fulfilled showing short settling time, reduced overshoot, and zero offset. This is an abstract of a paper presented at the 7th World Congress of Chemical Engineering (Glasgow, Scotland 7/10-14/2005).

AB - A statistical model using neural networks (NN) has been developed for an industrial FCC unit (FCCU) of Universal Oil Products type. The emerged NN model was used to implement FCCU control using nonlinear model predictive control (NMPC) algorithm. The control performance of the NMPC based on NN model was studied in the presence of representative disturbances. Both control performance requirements, setpoint tracking, and disturbance rejection, were fulfilled showing short settling time, reduced overshoot, and zero offset. This is an abstract of a paper presented at the 7th World Congress of Chemical Engineering (Glasgow, Scotland 7/10-14/2005).

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