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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20200 - Electrical engineering, Electronic engineering, Information engineering

Project

<a href="/en/project/TN02000025" target="_blank" >TN02000025: National Centre for Energy II</a><br>

Continuities

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

Publication year

2025

Confidentiality

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

Name of the periodical

Scientific Reports

ISSN

2045-2322

e-ISSN

2045-2322

Volume of the periodical

15

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

23

Pages from-to

1-23

UT code for WoS article

001522007200024

EID of the result in the Scopus database

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