A Deep Neural Network-Based Approach for Optimizing Ammonia–Hydrogen Combustion Mechanism

Xiaoting Xu, Jie Zhong, Yuchen Hu, Ridong Zhang, Kaiqi Zhang, Yunliang Qi () and Zhi Wang
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Xiaoting Xu: School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Jie Zhong: National Key Laboratory of Marine Engine Science and Technology, Shanghai 201108, China
Yuchen Hu: National Key Laboratory of Marine Engine Science and Technology, Shanghai 201108, China
Ridong Zhang: School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Kaiqi Zhang: School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Yunliang Qi: School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Zhi Wang: School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China

Energies, 2025, vol. 18, issue 22, 1-30

Abstract: Ammonia is a highly promising zero-carbon fuel for engines. However, it exhibits high ignition energy, slow flame propagation, and severe pollutant emissions, so it is usually burned in combination with highly reactive fuels such as hydrogen. An accurate understanding and modeling of ammonia–hydrogen combustion is of fundamental and practical significance to its application. Deep Neural Networks (DNNs) demonstrate significant potential in autonomously learning the interactions between high-dimensional inputs. This study proposed a deep neural network-based method for optimizing chemical reaction mechanism parameters, producing an optimized mechanism file as the final output. The novelty lies in two aspects: first, it systematically compares three DNN structures (Multi-layer perceptron (MLP), Convolutional Neural Network, and Residual Regression Neural Network (ResNet)) with other machine learning models (generalized linear regression (GLR), support vector machine (SVM), random forest (RF)) to identify the most effective structure for mapping combustion-related variables; second, it develops a ResNet-based surrogate model for ammonia–hydrogen mechanism optimization. For the test set (20% of the total dataset), the ResNet outperformed all other ML models and empirical correlations, achieving a coefficient of determination (R 2 ) of 0.9923 and root mean square error (RMSE) of 135. The surrogate model uses the trained ResNet to optimize mechanism parameters based on a Stagni mechanism by mapping the initial conditions to experimental IDT. The results show that the optimized mechanism improves the prediction accuracy on laminar flame speed (LFS) by approximately 36.6% compared to the original mechanism. This method, while initially applied to the optimization of an ammonia–hydrogen combustion mechanism, can potentially be adapted to optimize mechanisms for other fuels.

Keywords: deep neural network; multi-parameter optimization; combustion mechanism; ammonia and hydrogen (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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