Prediction of NOx emission from two-stage combustion of NH3-H2 mixtures under various conditions using artificial neural networks

被引:7
作者
Mao, Gongping [1 ]
Shi, Tiancheng [1 ]
Mao, Chenlin [2 ]
Wang, Ping [2 ]
机构
[1] Jiangsu Univ, Sch Automot & Traff Engn, Zhenjiang, Peoples R China
[2] Jiangsu Univ, Inst Energy Res, Zhenjiang, Peoples R China
基金
中国博士后科学基金;
关键词
NOx emissions; Rich-quench-lean two-stage combustion; NH3-H2; mixtures; Artificial neural networks; Performance evaluation; HYDROGEN; AMMONIA; FLAMES; GRNN;
D O I
10.1016/j.ijhydene.2023.10.129
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
This study aims to predict NOx emission from the combustion of ammonia (NH3) and hydrogen (H2) mixtures using Artificial Neural Network (ANN) models. Chemical Reactor Network (CRN) models were utilized to calculate the NOx emission during the Rich-Quench-Lean (RQL) two-stage combustion of NH3-H2 mixtures under various conditions, such as fuel pressures, inlet temperatures, primary air humidification fractions, total equivalent ratios and primary equivalent ratios. The simulation results served as a data set for training and validation of three different ANN models: Backpropagation (BP), Radial Basis Function (RBF), and Generalized Regression Neural Network (GRNN). The results show that all three ANN models can effectively replace the CRN model in accurately predicting NOx concentrations. In particular, the BP model, optimized with 37 hidden neurons, showcased superior performance, achieving a correlation coefficient (R) value of 0.99826 with the simulated results. It had the lowest Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values of 61.3 and 96.1, respectively. In addition, it achieved the highest coefficient of determination (R2) and Model Efficiency (ME) values of 0.9950 and 0.9908 respectively, surpassing the RBF and GRNN models. These results suggest that the BP model has significant potential as an alternative to the CRN model for predicting NOx emissions from two-stage combustion of NH3-H2 mixtures, demonstrating both accuracy and efficiency.
引用
收藏
页码:1414 / 1424
页数:11
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