Improving the Diagnosis Accuracy of Hydrothermal Aging Degree of V2O5/WO3–TiO2 Catalyst in SCR Control System Using an GS–PSO–SVM Algorithm

摘要: Selective catalytic reduction (SCR) is one of the most effective technologies used for eliminating NOx from diesel engines. This paper presents a novel method based on support vector machine (SVM) and particle swarm optimization (PSO) with grid search (GS) to diagnose degree aging V2O5/WO3–TiO2 catalyst in SCR system. study shows effect performance NH3 slip closed-loop control system under different factors (α), which are defined by reaction rate ( R scr ). A diagnosis GS–PSO–SVM has been presented as compared SVM, GS–SVM PSO–SVM get reliable results. The results show that average prediction accuracy up 93.8%, 93.1%, 92.9% 92.0% GS–PSO–SVM, PSO–SVM, SVM respectively. It demonstrated able identify catalyst’s aging, ultimately assist fault tolerance catalyst.