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- Title
Improving the Diagnosis Accuracy of Hydrothermal Aging Degree of V<sub>2</sub>O<sub>5</sub>/WO<sub>3</sub>-TiO<sub>2</sub> Catalyst in SCR Control System Using an GS-PSO-SVM Algorithm.
- Authors
Jie Hu; Jiawei Zeng; Li Wei; Fuwu Yan
- Abstract
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 a support vector machine (SVM) and particle swarm optimization (PSO) with grid search (GS) to diagnose the degree of aging of the V2O5/WO3-TiO2 catalyst in the SCR system. This study shows the aging effect on the performance of a NH3 slip based closed-loop SCR control system under different aging factors (α), which are defined by the SCR reaction rate (Rscr). 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.
- Subjects
HYDROTHERMAL alteration; DIESEL motors; VECTOR analysis
- Publication
Sustainability (2071-1050), 2017, Vol 9, Issue 4, p611
- ISSN
2071-1050
- Publication type
Article
- DOI
10.3390/su9040611