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

• Published in: International journal of automotive technology 2024, 25(1), 137, pp.97-106
• Main Authors: Zhu, Qiang, Li, Jiehui
• Format: Article
• Language: English
• Subjects: Accuracy; Ammonia; Automotive Engineering; Back propagation networks; Chemical reduction; Diesel engines; Emissions; Engine and Emissions; Engineering; Fluid and Thermal Engineering; Fuels and Lubricants; Genetic algorithms; Heat Transfer; Leakage; Model accuracy; Neural networks; Prediction models; Selective catalytic reduction; Ureas; 자동차공학
• ISSN: 1229-9138; 1976-3832
• DOI: 10.1007/s12239-024-00016-8

In selective catalytic reduction (SCR) systems, the urea injection control strategy is central to the control of NO x emissions. When urea is over-injected, ammonia leakage will occur downstream of the SCR. A neural network-based NH 3 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 NH 3 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 NH 3 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 NH 3 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 NH 3 leakage prediction is feasible, and GA-GRU model is better than GA-BP model in predicting NH 3 leakage. This provides the basis for developing DCU to reduce NH 3 leakage.