Optimization of the Uniformity Index Performance in the Selective Catalytic Reduction System Using a Metamodel

Abstract

The significance of the selective catalytic reduction system in vehicles increases in line with the high standards of emission control and enhanced selective catalytic reduction efficiency. This study aims to improve the performance of the selective catalytic reduction system through an optimization method using a metamodel. The objective function is defined as the ammonia uniformity index, and the design parameters are defined in relation to the pipe length and mixer related to the chemical reaction of the urea solution. The range of design parameters has been designated by a trial-and-error method in order to maintain the overall design drawings of the selective catalytic reduction system and prevent modeling errors. Three algorithms, namely, ensemble decision tree, Kriging, and radial basis function, are employed to develop the metamodel. The accuracy of the metamodel is verified based on three indicators: the normalized root mean square error, root mean square error, and maximum absolute error. The metamodel is generated using the Kriging model, which has the highest accuracy among the algorithms, and optimization is also performed. The predicted optimization results are confirmed by computational fluid dynamics numerical analysis with a 99.83% match. The ammonia uniformity index is improved by 1.38% compared to the base model, and it can be said that the NOx purification efficiency is improved by 30.95%. Consequently, optimizing the uniformity index performance through structural optimization is of utmost importance. Furthermore, this study reveals that the design variables related to the mixer play a crucial role in the performance. Therefore, using the metamodel to optimize the selectively catalytic reduction system’s structure should be considered significant. Finally, in the future, the analysis model can be validated using test equipment based on the findings of this study.