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Neural Network-Based Prediction of NH3 Leakage from SCR Systems for Diesel Engines

  • Engine and Emissions, Fuels and Lubricants, Heat Transfer, Fluid and Thermal Engineering
  • Published:
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Abstract

In selective catalytic reduction (SCR) systems, the urea injection control strategy is central to the control of NOx emissions. When urea is over-injected, ammonia leakage will occur downstream of the SCR. A neural network-based NH3 leakage prediction model for diesel engine SCR systems is proposed in order that the dosing control unit (DCU) can reduce the corresponding urea injection volume according to the NH3 leakage when calculating the urea injection volume. Back propagation (BP) neural network model and gated recurrent unit (GRU) model are developed respectively by code compilation software to predict the NH3 leakage. The genetic algorithm (GA) is used to find the optimal parameters of the two different models. Bench tests are conducted to evaluate the model accuracy. Under historical test data, the root mean square errors of the final GA-BP and GA-GRU models are 3.142 ppm and 2.378 ppm, respectively. The percentage of cumulative NH3 leakage prediction error of GA-BP and GA-GRU are 4.808% and 3.745%, respectively. The results show that the method of using neural network for NH3 leakage prediction is feasible, and GA-GRU model is better than GA-BP model in predicting NH3 leakage. This provides the basis for developing DCU to reduce NH3 leakage.

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Data availability

The data that support the findings of this study are available from the corresponding author, [Jiehui Li], upon reasonable request.

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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  1. School of Automotive and Traffic Engineering, Jiangsu University, 301 Xuefu Road, ZhenJiang, 212013, China

    Qiang Zhu & Jiehui Li

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  1. Qiang Zhu
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  2. Jiehui Li
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Correspondence to Jiehui Li.

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Zhu, Q., Li, J. Neural Network-Based Prediction of NH3 Leakage from SCR Systems for Diesel Engines. Int.J Automot. Technol. 25, 97–106 (2024). https://doi.org/10.1007/s12239-024-00016-8

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