Development of Machine Learning Algorithms for Application in Major Performance Enhancement in the Selective Catalytic Reduction (SCR) System

Machine learning is used in this study to deal with the reduction in the design period and major performance improvement of the selective catalyst reduction system. The selective catalyst reduction system helps in the reduction in NOx emission in the diesel engine. The existing methods for the design and performance improvement of selective catalyst reduction systems tend to be inefficient, due to layout changes that require modification when mounting a vehicle based on previously designed models. There are some factors that can affect the design of the diesel engine selective catalyst reduction system that can be identified by applying an optimized design. The Taguchi orthogonal array design is used with the eight factors and three levels of the main design factors. The distance of the urea injector, the distance of the mixer, the inflow angle of the exhaust gas, the angle of the urea injector, the angle of the mixer, the mounting angle in the direction of rotation of the mixer inside the selective catalyst reduction pipe, the number of mixer blades, the and bending angle of the mixer blade are identified as the eight major factors involved. These factors can also be considered manufacturing factors and can be established through machine learning. Machine learning has the advantage of being more efficient compared to other methods in determining the relationship between the data for each mutual factor. Machine learning can help in reducing processing time, which can further decrease the cost of the design analysis and improve the performance of the selective catalyst reduction system. This study shows that the results are statistically significant as the p values of the mixer blade number and cone length are lower than 0.05.

Automated summary

Machine learning is applied to reduce the design period and improve the performance of the selective catalyst reduction system. This study identifies the factors affecting the design of the system and uses the Taguchi orthogonal array design to optimize the main design factors. Machine learning proves to be more efficient in determining the relationship between the factors and reduces processing time, leading to cost reduction and performance improvement.