Digital Twin for Experimental Data Fusion Applied to a Semi-Industrial Furnace Fed with H2-Rich Fuel Mixtures

Procacci, Alberto; Cafiero, Marianna; Sharma, Saurabh; Kamal, Muhammad Mustafa; Coussement, Axel; Parente, Alessandro

doi:10.3390/en16020662

Open AccessArticle

Digital Twin for Experimental Data Fusion Applied to a Semi-Industrial Furnace Fed with H₂-Rich Fuel Mixtures

by

Alberto Procacci

^1,2,*

,

Marianna Cafiero

^1,2,3,

Saurabh Sharma

^1,2,

Muhammad Mustafa Kamal

^1,2,

Axel Coussement

^1,2

and

Alessandro Parente

^1,2

¹

Aero-Thermo-Mechanics Laboratory, École Polytechnique de Bruxelles, Université Libre de Bruxelles, 1050 Brussels, Belgium

²

Brussels Institute for Thermal-Fluid Systems and Clean Energy (BRITE), Université Libre de Bruxelles and Vrije Universiteit Brussel, 1050 Brussels, Belgium

³

Institute of Mechanics, Materials, and Civil Engineering, Université Catholique de Louvain, Place du Levant 2, 1348 Louvain-la-Neuve, Belgium

^*

Author to whom correspondence should be addressed.

Energies 2023, 16(2), 662; https://doi.org/10.3390/en16020662

Submission received: 30 November 2022 / Revised: 21 December 2022 / Accepted: 29 December 2022 / Published: 5 January 2023

(This article belongs to the Special Issue Heat Transfer Analysis and Modeling in Furnaces and Boilers)

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Abstract

The objective of this work is to build a Digital Twin of a semi-industrial furnace using Gaussian Process Regression coupled with dimensionality reduction via Proper Orthogonal Decomposition. The Digital Twin is capable of integrating different sources of information, such as temperature, chemiluminescence intensity and species concentration at the outlet. The parameters selected to build the design space are the equivalence ratio and the benzene concentration in the fuel stream. The fuel consists of a

H_{2}

/

{CH}_{4}

/

CO

blend, doped with a progressive addition of

C_{6} H_{6}

. It is an

H_{2}

-rich fuel mixture, representing a surrogate of a more complex Coke Oven Gas industrial mixture. The experimental measurements include the flame temperature distribution, measured on a

6 \times 8

grid using an air-cooled suction pyrometer, spatially resolved chemiluminescence measurements of

{OH}^{*}

and

{CH}^{*}

, and the species concentration (i.e.,

NO

,

{NO}_{2}

,

CO

,

H_{2} O

,

{CO}_{2}

,

O_{2}

) measured in the exhaust gases. The GPR-based Digital Twin approach has already been successfully applied on numerical datasets coming from CFD simulations. In this work, we demonstrate that the same approach can be applied on heterogeneous datasets, obtained from experimental measurements.

Keywords: digital twin; data fusion; dimensionality reduction

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MDPI and ACS Style

Procacci, A.; Cafiero, M.; Sharma, S.; Kamal, M.M.; Coussement, A.; Parente, A. Digital Twin for Experimental Data Fusion Applied to a Semi-Industrial Furnace Fed with H₂-Rich Fuel Mixtures. Energies 2023, 16, 662. https://doi.org/10.3390/en16020662

AMA Style

Procacci A, Cafiero M, Sharma S, Kamal MM, Coussement A, Parente A. Digital Twin for Experimental Data Fusion Applied to a Semi-Industrial Furnace Fed with H₂-Rich Fuel Mixtures. Energies. 2023; 16(2):662. https://doi.org/10.3390/en16020662

Chicago/Turabian Style

Procacci, Alberto, Marianna Cafiero, Saurabh Sharma, Muhammad Mustafa Kamal, Axel Coussement, and Alessandro Parente. 2023. "Digital Twin for Experimental Data Fusion Applied to a Semi-Industrial Furnace Fed with H₂-Rich Fuel Mixtures" Energies 16, no. 2: 662. https://doi.org/10.3390/en16020662

APA Style

Procacci, A., Cafiero, M., Sharma, S., Kamal, M. M., Coussement, A., & Parente, A. (2023). Digital Twin for Experimental Data Fusion Applied to a Semi-Industrial Furnace Fed with H₂-Rich Fuel Mixtures. Energies, 16(2), 662. https://doi.org/10.3390/en16020662

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Digital Twin for Experimental Data Fusion Applied to a Semi-Industrial Furnace Fed with H₂-Rich Fuel Mixtures

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