Improving the Diagnosis Accuracy of Hydrothermal Aging Degree of V 2 O 5 /WO 3 –TiO 2 Catalyst in SCR Control System Using an GS–PSO–SVM Algorithm

Selective catalytic reduction (SCR) is one of the most effective technologies used for eliminating NO x from diesel engines. This paper presents a novel method based on a support vector machine (SVM) and particle swarm optimization (PSO) with grid search (GS) to diagnose the degree of aging of the V 2 O 5 /WO 3 –TiO 2 catalyst in the SCR system. This study shows the aging effect on the performance of a NH 3 slip based closed-loop SCR control system under different aging factors ( α ), which are defined by the SCR reaction rate ( R scr ). A diagnosis of the performance of GS–PSO–SVM has been presented as compared to SVM, GS–SVM and PSO–SVM to get reliable results. The results show that the average prediction diagnosis accuracy of the degree of catalytic aging is up to 93.8%, 93.1%, 92.9% and 92.0% for GS–PSO–SVM, PSO–SVM, GS–SVM and SVM respectively. It is demonstrated that GS–PSO–SVM is able to identify the SCR catalyst’s degree of aging, to ultimately assist with fault tolerance in the aging of the SCR catalyst.