Work

An extrapolative machine learning framework for forecasting nitrogen oxide mitigation in alcohol enhanced ammonia-methane blends

Year: 2026

Type: article

Abstract: As carbon-free energy carrier, combustion of ammonia suffers from low burning rates, poor flame stability, and excessive nitrogen oxide (NOx) emissions. Although blending with hydrocarbon fuels such as methane alleviates some drawbacks, NOx formation remains a critical barrier. To address these challenges, we propose a hybrid framework combining reactive force field molecular dynamics (ReaxFF-MD) simulations with machine learning (ML). MD simulations at 2000-3000 K were performed for ammonia-methane blended combustion with 0-10% addition of ethanol or methanol. Adding alcohols suppressed the NOx formation by altering charge redistributions and redirecting nitrogen intermediates into stabilising pathways. Particularly at 3000 K, 10% ethanol and methanol reduced NOx by ∼39.5% and ∼30.1%, respectively. Both chemical and physical descriptors derived from MD were used to train ML models and successfully predicted NOx trends at intermediate compositions (2%, 7%, 12%) with <5% error for ethanol-rich mixtures, though predictions beyond 12% require further validation. This framework reduces reliance on costly simulations while providing mechanistic insights and predictive capability of designing alternative fuels.

Source: Energy and AI

DOI: 10.1016/j.egyai.2026.100705

Authors: Amirali Shateri, Zhiyin Yang, Lei Xing, Yuying Yan, Zhen Wu, Jianfei Xie

Institutions: University of Derby, University of Surrey, University of Nottingham, Xi'an Jiaotong University

Language: English

Topic: Advanced Combustion Engine Technologies

Subfield: Fluid Flow and Transfer Processes

Field: Chemical Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action

Open Access status: gold

Estimated APC paid: $3,630