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

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

6 Scopus citations

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.

Original language	English
Title of host publication	2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-6
Number of pages	6
ISBN (Electronic)	9781538608197
DOIs	https://doi.org/10.1109/ROPEC.2017.8261618
State	Published - 1 Jul 2017
Event	2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017 - Ixtapa, Guerrero, Mexico Duration: 8 Nov 2017 → 10 Nov 2017

Publication series

Name	2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017
Volume	2018-January

Conference

Conference	2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017
Country/Territory	Mexico
City	Ixtapa, Guerrero
Period	8/11/17 → 10/11/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Energy Reduction
Model Predictive Control
PID control
System Identification

Access to Document

10.1109/ROPEC.2017.8261618

Cite this

Caiza, L., Rosich-Oliva, A., Ocampo-Martinez, C., & Benitez, D. S. (2017). Reducing energy consumption in an industrial process by using model predictive control. In 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.\}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017 ; Conference date: 08-11-2017 Through 10-11-2017",

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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. in 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, Mexico, 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).

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

TY - GEN

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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

SP - 1

EP - 6

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. In 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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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