Modeling and optimization of ammonia/hydrogen/air premixed swirling flames for NOx emission control - A hybrid machine learning strategy

Aug 5, 2026

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Hydrogen and ammonia are assessed as alternative fuels to support low-emission energy goals, with ammonia emphasized for its zero-carbon emissions and high energy density. A hybrid machine learning workflow applies XGBoost and SVR to predict NOx emissions and flame temperature for ammonia/hydrogen blends, achieving predominantly R2 values above 0.97 and a best-performing low MSE of 3508.31 with R2 of 0.97653. Feature importance analysis identifies NH3 mole proportion, equivalence ratio, and total mass flow rate as key drivers of nitrogen emissions. The XSN optimization framework further reduces nitrogen gases concentration by 51.91%, from 69.81 ppm to 33.57 ppm, supporting stable operation while meeting multiple optimization objectives for practical NH3/H2 combustion.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Introduction

Motivation for alternative fuels

Challenges of ammonia combustion

Need to reduce NOx emissions

Modeling approach

XGBoost and SVR prediction

Feature importance analysis

Optimization framework

XSN optimization results

Reduction in nitrogen gases

Conclusions and implications

Energy 330 (2025) 136735 Contents lists available at ScienceDirect Energy journal [homepage:](homepage: www.elsevier.com/locate/energy)[ www.elsevier.com/locate/energy](homepage: www.elsevier.com/locate/energy) | Modeling and optimization of ammonia/hydrogen/air premixed swirling flames for NOx emission control: A hybrid machine learning strategy | | | | | --- | --- | --- | --- | | Hao Shia,b , Zebang Liu a,* , Syed Mashruka , Mohammad Alnajideena , Ali Alnasifa,c, Jing Liu d, Agustin Valera-Medina a
a School of Engineering, Cardiff University, Queen’s Building, Cardiff, CF24 3AA, United Kingdom
b Technical University of Darmstadt, Department of Mechanical Engineering, Reactive Flows and Diagnostics, Otto-Berndt-Straße 3, 64287, Darmstadt, Germany c Department of Aeronautical Techniques, Engineering Technical College of Al-Najaf, Al-Furat Al-Awsat Technical University, Najaf, 31001, Iraq
d Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China | | | | | A R T I C L E I N F O | | A B S T R A C T | | | Handling editor: P Ferreira | | In the face of escalating climate change concerns, the quest for sustainable energy solutions is more pressing than ever. This study delves into the potential of hydrogen and ammonia as alternative fuels, with a focus on ammonia’s promise due to its zero-carbon emissions and high energy density. Employing machine learning techniques, specifically XGBoost and SVR, this study presents a comprehensive analysis of ammonia and hydrogen fuel blends to predict NOx emissions and flame temperature with high accuracy, achieving R2 values predominantly above 0.97. The model’s precision is particularly noteworthy compared to other machine learning techniques, where it consistently outperforms with the lowest MSE of 3508.31 and an impressive R2 value of 0.97653. A detailed feature importance analysis underscores the significance of NH3 mole proportion, equivalence ratio, and total mass flow rate in influencing nitrogen emissions. Furthermore, the proposed XSN optimization framework has proven effective in reducing nitrogen compounds, achieving a substantial decrease in Ngases concentration by 51.91 %, from 69.81 ppm to 33.57 ppm. The hybrid model developed in this study demonstrates exceptional capability in managing multiple optimization objectives, thereby offering advantagesin reducing the overall harmful emissions while maintaining stable operation in practical applications of NH3/H2 combustion. This research enhances the accuracy of emissions prediction under diverse conditions and provides valuable insights into effective strategies for controlling nitrogen emissions from NH3/H2 combustion. | | | Keywords:
Ammonia/hydrogen combustion NOx reduction
Nitrogen emissions
Machine learning Optimization | | | | 1. Introduction According to the International Energy Agency (IEA), the global energy demand is projected to triple in the next decade and quintuple by the mid-century [1]. However, the increasing energy consumption, along with the rising carbon dioxide levels in the atmosphere, poses a serious and complex challenge for humanity. The threat of climate change and global warming urges the transition to a low-emission, carbon-free economy. Therefore, the search for clean and renewable energy sources drives the investigation of alternative non-fossil fuels, such as hydrogen. However, hydrogen as a fuel has inherent challenges of storage and distribution [2,3]. In contrast, ammonia is regarded as a promising candidate for the future energy sector due to its superior properties such as zero-carbon emission, easy storage, high energy density, etc. Furthermore, ammonia in combustion systems is becoming a viable option for replacing fossil fuels and reducing carbon emissions [4,5]. However, the widespread adoption of ammonia as a fuel still faces several challenges, such as its hig

FAQs

Which machine learning methods are used to predict NOx emissions and flame temperature?

The study employs XGBoost and SVR in a hybrid strategy to predict NOx emissions and flame temperature for ammonia/hydrogen/air premixed swirling flames.

How accurate is the hybrid model according to the reported metrics?

Predictions achieve predominantly R2 values above 0.97, with the best results showing the lowest MSE of 3508.31 and an R2 value of 0.97653.

What parameters most influence nitrogen emissions in the feature importance analysis?

NH3 mole proportion, equivalence ratio, and total mass flow rate are identified as the most significant factors affecting nitrogen emissions.

Aug 5, 2026

Upload by Skyler