Machine learning assisted CFD optimization of fuel-staging natural gas burners for enhanced combustion efficiency and reduced NOx emissions

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27730%2F25%3A10258045" target="_blank" >RIV/61989100:27730/25:10258045 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.nature.com/articles/s41598-025-05132-8" target="_blank" >https://www.nature.com/articles/s41598-025-05132-8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-025-05132-8" target="_blank" >10.1038/s41598-025-05132-8</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine learning assisted CFD optimization of fuel-staging natural gas burners for enhanced combustion efficiency and reduced NOx emissions

Popis výsledku v původním jazyce

Industrial combustion systems are among the primary contributors to nitrogen oxide (NOx) emissions, posing challenges for air quality management and regulatory compliance. This study presents a computational and data-driven approach to the design and optimization of a natural gas burner employing a folded flame pattern with fuel staging. Using Computational Fluid Dynamics (CFD) simulations combined with Machine Learning (ML)-assisted predictive modeling, the burner geometry, fuel-air mixing behavior, and heat transfer dynamics were systematically optimized. A Support Vector Regression-based model was trained on CFD-generated data to guide design modifications and reduce reliance on trial-and-error experimentation. The resulting burner design achieved a 31% reduction in NOx emissions while maintaining combustion efficiency and improving flame stability. Lower peak flame temperatures contributed to reduced pollutant formation. Particle tracing analysis revealed recirculation zones that promoted optimal fuel-air mixing and heat transfer. This integrated CFD-ML framework demonstrates a scalable solution for cleaner combustion design. Future work will focus on experimental validation and the adaptability of the burner to alternative fuels such as hydrogen-rich blends and biogas, aiming to extend the applicability of this approach across diverse industrial settings.

Název v anglickém jazyce

Machine learning assisted CFD optimization of fuel-staging natural gas burners for enhanced combustion efficiency and reduced NOx emissions

Popis výsledku anglicky

Industrial combustion systems are among the primary contributors to nitrogen oxide (NOx) emissions, posing challenges for air quality management and regulatory compliance. This study presents a computational and data-driven approach to the design and optimization of a natural gas burner employing a folded flame pattern with fuel staging. Using Computational Fluid Dynamics (CFD) simulations combined with Machine Learning (ML)-assisted predictive modeling, the burner geometry, fuel-air mixing behavior, and heat transfer dynamics were systematically optimized. A Support Vector Regression-based model was trained on CFD-generated data to guide design modifications and reduce reliance on trial-and-error experimentation. The resulting burner design achieved a 31% reduction in NOx emissions while maintaining combustion efficiency and improving flame stability. Lower peak flame temperatures contributed to reduced pollutant formation. Particle tracing analysis revealed recirculation zones that promoted optimal fuel-air mixing and heat transfer. This integrated CFD-ML framework demonstrates a scalable solution for cleaner combustion design. Future work will focus on experimental validation and the adaptability of the burner to alternative fuels such as hydrogen-rich blends and biogas, aiming to extend the applicability of this approach across diverse industrial settings.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

<a href="/cs/project/TN02000025" target="_blank" >TN02000025: Národní centrum pro energetiku II</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Scientific Reports

ISSN

2045-2322

e-ISSN

2045-2322

Svazek periodika

15

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

23

Strana od-do

1-23

Kód UT WoS článku

001522007200024

EID výsledku v databázi Scopus

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