Nonlinear model predictive control for the industrial BioPower 5 CHP plant

Jukka Kortela

doi:10.1109/ETFA52439.2022.9921719

Nonlinear model predictive control for the industrial BioPower 5 CHP plant

Jukka Kortela^*

^*Corresponding author for this work

Department of Chemical and Metallurgical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

Abstract

In the operation of a biopower plant, a crucial role is played by the residual oxygen content in the flue gas. The flue gas oxygen content, depending on the total air supply and on the fuel composition, provides the information needed to estimate the power developed by the combustion. Therefore, in control systems that operate power plants, the flue gas oxygen content is directly measured and represents a key feedback variable. The novel nonlinear model predictive control developed is based on the model of the BioPower 5 CHP plant which also includes the nonlinear model of the flue gas oxygen content. In addition, the fast response is achieved by regulating primary air flow. To verify the model, experiments were performed at a biopower plant, which utilizes BioGrate combustion technology to enable the use of wet biomass fuels with a moisture content as high as 65%. Then the nonlinear model predictive control was tested in the simulated environment. Finally, the results are presented, analyzed, and discussed.

Original language	English
Title of host publication	2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation, ETFA 2022
Publisher	IEEE
Number of pages	4
ISBN (Electronic)	978-1-6654-9996-5
ISBN (Print)	978-1-6654-9997-2
DOIs	https://doi.org/10.1109/ETFA52439.2022.9921719
Publication status	Published - 25 Oct 2022
MoE publication type	A4 Conference publication
Event	IEEE International Conference on Emerging Technologies and Factory Automation - Stuttgart, Germany Duration: 6 Sept 2022 → 9 Sept 2022 Conference number: 27

Publication series

Name	IEEE International Conference on Emerging Technologies and Factory Automation, ETFA
Volume	2022-September
ISSN (Print)	1946-0740
ISSN (Electronic)	1946-0759

Conference

Conference	IEEE International Conference on Emerging Technologies and Factory Automation
Abbreviated title	EFTA
Country/Territory	Germany
City	Stuttgart
Period	06/09/2022 → 09/09/2022

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

NMPC
nonlinear model predictive control
biomass
industrial process

Access to Document

10.1109/ETFA52439.2022.9921719

Cite this

Kortela, J. (2022). Nonlinear model predictive control for the industrial BioPower 5 CHP plant. In 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation, ETFA 2022 Article 22187439 (IEEE International Conference on Emerging Technologies and Factory Automation, ETFA; Vol. 2022-September). IEEE. https://doi.org/10.1109/ETFA52439.2022.9921719

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title = "Nonlinear model predictive control for the industrial BioPower 5 CHP plant",

abstract = "In the operation of a biopower plant, a crucial role is played by the residual oxygen content in the flue gas. The flue gas oxygen content, depending on the total air supply and on the fuel composition, provides the information needed to estimate the power developed by the combustion. Therefore, in control systems that operate power plants, the flue gas oxygen content is directly measured and represents a key feedback variable. The novel nonlinear model predictive control developed is based on the model of the BioPower 5 CHP plant which also includes the nonlinear model of the flue gas oxygen content. In addition, the fast response is achieved by regulating primary air flow. To verify the model, experiments were performed at a biopower plant, which utilizes BioGrate combustion technology to enable the use of wet biomass fuels with a moisture content as high as 65\%. Then the nonlinear model predictive control was tested in the simulated environment. Finally, the results are presented, analyzed, and discussed.",

keywords = "NMPC, nonlinear model predictive control, biomass, industrial process",

author = "Jukka Kortela",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; IEEE International Conference on Emerging Technologies and Factory Automation, EFTA ; Conference date: 06-09-2022 Through 09-09-2022",

year = "2022",

month = oct,

day = "25",

doi = "10.1109/ETFA52439.2022.9921719",

language = "English",

isbn = "978-1-6654-9997-2",

series = "IEEE International Conference on Emerging Technologies and Factory Automation, ETFA",

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booktitle = "2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation, ETFA 2022",

address = "United States",

}

Kortela, J 2022, Nonlinear model predictive control for the industrial BioPower 5 CHP plant. in 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation, ETFA 2022., 22187439, IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, vol. 2022-September, IEEE, IEEE International Conference on Emerging Technologies and Factory Automation, Stuttgart, Baden-Württemberg, Germany, 06/09/2022. https://doi.org/10.1109/ETFA52439.2022.9921719

Nonlinear model predictive control for the industrial BioPower 5 CHP plant. / Kortela, Jukka.
2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation, ETFA 2022. IEEE, 2022. 22187439 (IEEE International Conference on Emerging Technologies and Factory Automation, ETFA; Vol. 2022-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Nonlinear model predictive control for the industrial BioPower 5 CHP plant

AU - Kortela, Jukka

N1 - Conference code: 27

PY - 2022/10/25

Y1 - 2022/10/25

N2 - In the operation of a biopower plant, a crucial role is played by the residual oxygen content in the flue gas. The flue gas oxygen content, depending on the total air supply and on the fuel composition, provides the information needed to estimate the power developed by the combustion. Therefore, in control systems that operate power plants, the flue gas oxygen content is directly measured and represents a key feedback variable. The novel nonlinear model predictive control developed is based on the model of the BioPower 5 CHP plant which also includes the nonlinear model of the flue gas oxygen content. In addition, the fast response is achieved by regulating primary air flow. To verify the model, experiments were performed at a biopower plant, which utilizes BioGrate combustion technology to enable the use of wet biomass fuels with a moisture content as high as 65%. Then the nonlinear model predictive control was tested in the simulated environment. Finally, the results are presented, analyzed, and discussed.

AB - In the operation of a biopower plant, a crucial role is played by the residual oxygen content in the flue gas. The flue gas oxygen content, depending on the total air supply and on the fuel composition, provides the information needed to estimate the power developed by the combustion. Therefore, in control systems that operate power plants, the flue gas oxygen content is directly measured and represents a key feedback variable. The novel nonlinear model predictive control developed is based on the model of the BioPower 5 CHP plant which also includes the nonlinear model of the flue gas oxygen content. In addition, the fast response is achieved by regulating primary air flow. To verify the model, experiments were performed at a biopower plant, which utilizes BioGrate combustion technology to enable the use of wet biomass fuels with a moisture content as high as 65%. Then the nonlinear model predictive control was tested in the simulated environment. Finally, the results are presented, analyzed, and discussed.

KW - NMPC

KW - nonlinear model predictive control

KW - biomass

KW - industrial process

UR - https://www.scopus.com/pages/publications/85141381193

U2 - 10.1109/ETFA52439.2022.9921719

DO - 10.1109/ETFA52439.2022.9921719

M3 - Conference article in proceedings

AN - SCOPUS:85141381193

SN - 978-1-6654-9997-2

T3 - IEEE International Conference on Emerging Technologies and Factory Automation, ETFA

BT - 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation, ETFA 2022

PB - IEEE

T2 - IEEE International Conference on Emerging Technologies and Factory Automation

Y2 - 6 September 2022 through 9 September 2022

ER -

Nonlinear model predictive control for the industrial BioPower 5 CHP plant

Abstract

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UN SDGs

Keywords

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