Reducing energy consumption in an industrial process by using model predictive control

Luis Caiza; Albert Rosich-Oliva; Carlos Ocampo-Martinez; Diego S. Benitez

doi:10.1109/ROPEC.2017.8261618

Reducing energy consumption in an industrial process by using model predictive control

Luis Caiza
, Albert Rosich-Oliva
, Carlos Ocampo-Martinez
, Diego S. Benitez

Ingeniería Electrónica y Automatización

Universidad San Francisco de Quito
Polytechnic University of Catalonia

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

6 Citas (Scopus)

Resumen

In this paper, we propose to use Model Predictive Control techniques to reduce the energy consumption in an industrial process by incorporating energy consumption restrictions in the problem formulation. We propose to use a Model Predictive Control supervisor to calculate the optimum references for simple control loops regulated by individual PID controllers. The results obtained are compared against those obtained when a traditional control strategy exclusively based on PID controllers was used. By using this supervisory control strategy a reduction on energy consumption of about 10% was achieved.

Idioma original	Inglés
Título de la publicación alojada	2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017
Editorial	Institute of Electrical and Electronics Engineers Inc.
Páginas	1-6
Número de páginas	6
ISBN (versión digital)	9781538608197
DOI	https://doi.org/10.1109/ROPEC.2017.8261618
Estado	Publicada - 1 jul 2017
Evento	2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017 - Ixtapa, Guerrero, México Duración: 8 nov 2017 → 10 nov 2017

Serie de la publicación

Nombre	2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017
Volumen	2018-January

Conferencia

Conferencia	2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017
País/Territorio	México
Ciudad	Ixtapa, Guerrero
Período	8/11/17 → 10/11/17

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 7: Energía asequible y no contaminante

Acceder al documento

10.1109/ROPEC.2017.8261618

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Enlace a la publicación en Scopus

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Caiza, L., Rosich-Oliva, A., Ocampo-Martinez, C., & Benitez, D. S. (2017). Reducing energy consumption in an industrial process by using model predictive control. En 2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017 (pp. 1-6). (2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ROPEC.2017.8261618

Caiza, Luis ; Rosich-Oliva, Albert ; Ocampo-Martinez, Carlos et al. / Reducing energy consumption in an industrial process by using model predictive control. 2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1-6 (2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017).

@inproceedings{779de0ec70864a93a33cd74ea539fb48,

title = "Reducing energy consumption in an industrial process by using model predictive control",

abstract = "In this paper, we propose to use Model Predictive Control techniques to reduce the energy consumption in an industrial process by incorporating energy consumption restrictions in the problem formulation. We propose to use a Model Predictive Control supervisor to calculate the optimum references for simple control loops regulated by individual PID controllers. The results obtained are compared against those obtained when a traditional control strategy exclusively based on PID controllers was used. By using this supervisory control strategy a reduction on energy consumption of about 10\% was achieved.",

keywords = "Energy Reduction, Model Predictive Control, PID control, System Identification",

author = "Luis Caiza and Albert Rosich-Oliva and Carlos Ocampo-Martinez and Benitez, \{Diego S.\}",

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Caiza, L, Rosich-Oliva, A, Ocampo-Martinez, C & Benitez, DS 2017, Reducing energy consumption in an industrial process by using model predictive control. En 2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017. 2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-6, 2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017, Ixtapa, Guerrero, México, 8/11/17. https://doi.org/10.1109/ROPEC.2017.8261618

Reducing energy consumption in an industrial process by using model predictive control. / Caiza, Luis; Rosich-Oliva, Albert; Ocampo-Martinez, Carlos et al.
2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1-6 (2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017; Vol. 2018-January).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Reducing energy consumption in an industrial process by using model predictive control

AU - Caiza, Luis

AU - Rosich-Oliva, Albert

AU - Ocampo-Martinez, Carlos

AU - Benitez, Diego S.

PY - 2017/7/1

Y1 - 2017/7/1

N2 - In this paper, we propose to use Model Predictive Control techniques to reduce the energy consumption in an industrial process by incorporating energy consumption restrictions in the problem formulation. We propose to use a Model Predictive Control supervisor to calculate the optimum references for simple control loops regulated by individual PID controllers. The results obtained are compared against those obtained when a traditional control strategy exclusively based on PID controllers was used. By using this supervisory control strategy a reduction on energy consumption of about 10% was achieved.

AB - In this paper, we propose to use Model Predictive Control techniques to reduce the energy consumption in an industrial process by incorporating energy consumption restrictions in the problem formulation. We propose to use a Model Predictive Control supervisor to calculate the optimum references for simple control loops regulated by individual PID controllers. The results obtained are compared against those obtained when a traditional control strategy exclusively based on PID controllers was used. By using this supervisory control strategy a reduction on energy consumption of about 10% was achieved.

KW - Energy Reduction

KW - Model Predictive Control

KW - PID control

KW - System Identification

UR - https://www.scopus.com/pages/publications/85046246712

U2 - 10.1109/ROPEC.2017.8261618

DO - 10.1109/ROPEC.2017.8261618

M3 - Contribución a la conferencia

AN - SCOPUS:85046246712

T3 - 2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017

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BT - 2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017

Y2 - 8 November 2017 through 10 November 2017

ER -

Caiza L, Rosich-Oliva A, Ocampo-Martinez C, Benitez DS. Reducing energy consumption in an industrial process by using model predictive control. En 2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1-6. (2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017). doi: 10.1109/ROPEC.2017.8261618

Reducing energy consumption in an industrial process by using model predictive control

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