Application of multivariable regression model for SOFC stack temperature estimation in system environment

M. Halinen, A. Pohjoranta (Corresponding Author), J. Pennanen, J. Kiviaho

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)

Abstract

The applicability of multivariable linear regression (MLR) models to estimate the maximum temperature inside a SOFC stack is investigated experimentally. The experiments were carried out with a complete 10 kW SOFC system. The behavior of the maximum temperature measured inside a SOFC stack with respect to four independent input variables (stack current, air flow, air inlet temperature and fuel flow) is examined following the design of experiments methodology, and MLR models are created based on the retrieved data. The practical feasibility of the MLR estimate is investigated experimentally with the 10 kW system by evaluating the accuracy of the estimate in two test cases: (i) a system load change where the stack temperature is regulated by a closed-loop controller using the MLR estimate and (ii) during operator-imposed disturbances in the fuel system (a variation in the methane conversion in the fuel pre-reformer). Finally, the performance of the MLR estimate is evaluated with another, 64-cell stack operated at higher current density.
Original languageEnglish
Pages (from-to)749-756
JournalFuel Cells
Volume15
Issue number5
DOIs
Publication statusPublished - 2015
MoE publication typeA1 Journal article-refereed
EventThe 11th European SOFC & SOE Forum - Lucerne, Switzerland
Duration: 1 Jul 20144 Jul 2014

Fingerprint

Solid oxide fuel cells (SOFC)
Linear regression
Temperature
Fuel systems
Air intakes
Design of experiments
Methane
Current density
Controllers
Air
Experiments

Keywords

  • control
  • fule cell
  • fuel cell system
  • mathematical modeling
  • SOFC

Cite this

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title = "Application of multivariable regression model for SOFC stack temperature estimation in system environment",
abstract = "The applicability of multivariable linear regression (MLR) models to estimate the maximum temperature inside a SOFC stack is investigated experimentally. The experiments were carried out with a complete 10 kW SOFC system. The behavior of the maximum temperature measured inside a SOFC stack with respect to four independent input variables (stack current, air flow, air inlet temperature and fuel flow) is examined following the design of experiments methodology, and MLR models are created based on the retrieved data. The practical feasibility of the MLR estimate is investigated experimentally with the 10 kW system by evaluating the accuracy of the estimate in two test cases: (i) a system load change where the stack temperature is regulated by a closed-loop controller using the MLR estimate and (ii) during operator-imposed disturbances in the fuel system (a variation in the methane conversion in the fuel pre-reformer). Finally, the performance of the MLR estimate is evaluated with another, 64-cell stack operated at higher current density.",
keywords = "control, fule cell, fuel cell system, mathematical modeling, SOFC",
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language = "English",
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journal = "Fuel Cells",
issn = "1615-6846",
publisher = "Wiley",
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Application of multivariable regression model for SOFC stack temperature estimation in system environment. / Halinen, M.; Pohjoranta, A. (Corresponding Author); Pennanen, J.; Kiviaho, J.

In: Fuel Cells, Vol. 15, No. 5, 2015, p. 749-756.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Application of multivariable regression model for SOFC stack temperature estimation in system environment

AU - Halinen, M.

AU - Pohjoranta, A.

AU - Pennanen, J.

AU - Kiviaho, J.

PY - 2015

Y1 - 2015

N2 - The applicability of multivariable linear regression (MLR) models to estimate the maximum temperature inside a SOFC stack is investigated experimentally. The experiments were carried out with a complete 10 kW SOFC system. The behavior of the maximum temperature measured inside a SOFC stack with respect to four independent input variables (stack current, air flow, air inlet temperature and fuel flow) is examined following the design of experiments methodology, and MLR models are created based on the retrieved data. The practical feasibility of the MLR estimate is investigated experimentally with the 10 kW system by evaluating the accuracy of the estimate in two test cases: (i) a system load change where the stack temperature is regulated by a closed-loop controller using the MLR estimate and (ii) during operator-imposed disturbances in the fuel system (a variation in the methane conversion in the fuel pre-reformer). Finally, the performance of the MLR estimate is evaluated with another, 64-cell stack operated at higher current density.

AB - The applicability of multivariable linear regression (MLR) models to estimate the maximum temperature inside a SOFC stack is investigated experimentally. The experiments were carried out with a complete 10 kW SOFC system. The behavior of the maximum temperature measured inside a SOFC stack with respect to four independent input variables (stack current, air flow, air inlet temperature and fuel flow) is examined following the design of experiments methodology, and MLR models are created based on the retrieved data. The practical feasibility of the MLR estimate is investigated experimentally with the 10 kW system by evaluating the accuracy of the estimate in two test cases: (i) a system load change where the stack temperature is regulated by a closed-loop controller using the MLR estimate and (ii) during operator-imposed disturbances in the fuel system (a variation in the methane conversion in the fuel pre-reformer). Finally, the performance of the MLR estimate is evaluated with another, 64-cell stack operated at higher current density.

KW - control

KW - fule cell

KW - fuel cell system

KW - mathematical modeling

KW - SOFC

UR - https://onlinelibrary.wiley.com/toc/16156854/2015/15/5

U2 - 10.1002/fuce.201500009

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VL - 15

SP - 749

EP - 756

JO - Fuel Cells

JF - Fuel Cells

SN - 1615-6846

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ER -