Abstract：
To reduce the number of sensors in the SCR catalyst, state feedback and fault diagnosis information are provided. Firstly, a model based on the coupling of flow, heat transfer, and gas-solid phase catalytic reaction in the SCR system is investigated in this paper. The parabolic partial differential equations are simplified by the variable substitution method and the method of lines approach (MOL). The simplified system of equations is solved by backward differentiation formulas (BDF) with adaptive adjustment time step strategy. Meanwhile, the chemical reaction parameters are accurately calibrated per second using the Levenberg-Marquardt method. Secondly, the ATS-UKF is designed in this paper, and to ensure the synchronisation between the ATS-UKF and the SCR model calculations, the time step of solving the BDF by the SCR model is taken as the time step of propagating the sigma points. Two observation scenarios are assumed: (1) no downstream NH3 concentration sensor, ammonia coverage and downstream NH3 concentration are observed by ATS-UKF; (2) no downstream NOx sensor, ammonia coverage and downstream NOx concentration are observed by ATS-UKF. Finally, the paper carries out bench tests. In the first case, the ammonia coverage obtained by the ATS-UKF reached 0.99 with respect to the model-calculated value R². The mean absolute error (MAE) between the observed and experimental values of the ATS-UKF for the downstream NH3 concentration was 2.76 ppm. In the second case, the ammonia coverage obtained by the ATS-UKF reached 0.99 with respect to the model-calculated value R², and the MAE between the observed and experimental values of the ATS-UKF for the downstream NOx concentration was 1.53 ppm. ENVIRONMENTAL IMPLICATION: The Adaptive Time-Step Unscented Kalman Filtering (ATS-UKF) enhances urea Selective Catalytic Reduction (SCR) in diesel engines, improving environmental outcomes. This method minimizes sensor dependence, enabling more precise SCR system management and effective emission reduction. By advancing emission control technologies, ATS-UKF contributes to global air pollution mitigation efforts, supporting cleaner air and environmental sustainability. Its innovative approach in monitoring and predicting SCR performance marks a significant step towards eco-friendly diesel engine operation.
摘要：
为了减少SCR催化器中的传感器数量，状态反馈和故障诊断信息。首先,基于流动耦合的模型，热传递,本文对SCR系统中的气固催化反应进行了研究。通过变量替代方法和直线方法（MOL）简化了抛物型偏微分方程。通过具有自适应调整时间步长策略的后向微分公式（BDF）求解简化的方程组。同时,使用Levenberg-Marquardt方法每秒精确校准化学反应参数。其次,本文设计了ATS-UKF，并确保ATS-UKF和SCR模型计算之间的同步，将SCR模型求解BDF的时间步长作为传播sigma点的时间步长。假设两种观测场景:(1)无下游NH3浓度传感器,ATS-UKF观察到氨覆盖率和下游NH3浓度;(2)没有下游NOx传感器,通过ATS-UKF观察到氨覆盖率和下游NOx浓度。最后,本文进行了台架试验。在第一种情况下,相对于模型计算值R²，ATS-UKF获得的氨覆盖率达到0.99。下游NH3浓度的ATS-UKF的观测值与实验值之间的平均绝对误差(MAE)为2.76ppm。在第二种情况下，ATS-UKF获得的氨覆盖率相对于模型计算值R²达到0.99，下游NOx浓度的ATS-UKF的观测值和实验值之间的MAE为1.53ppm。环境含义：自适应时间步长无迹卡尔曼滤波（ATS-UKF）增强了柴油发动机中的尿素选择性催化还原（SCR），改善环境结果。这种方法最大限度地减少传感器的依赖性，实现更精确的SCR系统管理和有效的减排。通过推进排放控制技术，ATS-UKF为全球空气污染缓解工作做出了贡献，支持更清洁的空气和环境可持续性。其在监测和预测SCR性能方面的创新方法标志着朝着环保柴油发动机运行迈出了重要一步。
