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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

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  • Jie Hu

    (Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430000, China
    Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan 430000, China)

  • Jiawei Zeng

    (Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430000, China
    Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan 430000, China)

  • Li Wei

    (Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430000, China
    Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan 430000, China)

  • Fuwu Yan

    (Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430000, China
    Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan 430000, China)

Abstract

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.

Suggested Citation

  • Jie Hu & Jiawei Zeng & Li Wei & Fuwu Yan, 2017. "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," Sustainability, MDPI, vol. 9(4), pages 1-15, April.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:4:p:611-:d:95864
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    References listed on IDEAS

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    1. Stoppato, Anna & Cavazzini, Giovanna & Ardizzon, Guido & Rossetti, Antonio, 2014. "A PSO (particle swarm optimization)-based model for the optimal management of a small PV(Photovoltaic)-pump hydro energy storage in a rural dry area," Energy, Elsevier, vol. 76(C), pages 168-174.
    2. Delgarm, N. & Sajadi, B. & Kowsary, F. & Delgarm, S., 2016. "Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO)," Applied Energy, Elsevier, vol. 170(C), pages 293-303.
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