Document Type : Original/Review Paper
Authors
Bozorgmehr University of Qaenat
Abstract
Hydrogen combustion has emerged as a pivotal technology for decarbonizing the energy sector, offering a clean and sustainable alternative to fossil fuels. This study investigates hydrogen combustion dynamics in a perfectly stirred reactor (PSR) under steady-state, non-premixed conditions. It is employing CHEMKIN-based simulations to analyze the effects of nitrogen dilution, operating pressure, and equivalence ratio on flame temperature and NOx emissions. The parametric results reveal that nitrogen dilution reduces flame temperature by up to 28% and suppresses NOx emissions by 15–40%, while elevated pressure promotes higher flame temperatures and increased NOx formation. Peak temperature and NOx concentrations are observed under stoichiometric conditions (φ = 1.0), with both quantities decreasing under lean and rich mixture conditions. To enable rapid and accurate prediction of these combustion characteristics, three machine learning models were developed and benchmarked on the CHEMKIN-generated dataset: Gaussian Process Regression, Multilayer Perceptron, and Deep Neural Network. GPR demonstrated the best overall predictive performance, achieving the lowest MAE for temperature prediction (MAE = 1.77) and major species concentrations. Although the DNN produced competitive results (MAE = 58.28), it demanded approximately five times more computational resources than GPR, without a proportional gain in predictive accuracy. All three models, maintained prediction errors below 20% across the investigated parameter space, confirming their viability as efficient tools for hydrogen combustion optimization. These findings demonstrate that physics-informed machine learning models, when combined with high-fidelity combustion simulations, offer a powerful and computationally efficient pathway toward accelerating the design and optimization of clean hydrogen energy systems.
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