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Modeling and Multi-Objective Optimization of NO x Conversion Efficiency and NH 3 Slip for a Diesel Engine

Author

Listed:
  • Bo Liu

    (Wuhan University of Technology, Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan 430070, China
    Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan 430070, China)

  • Fuwu Yan

    (Wuhan University of Technology, Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan 430070, China
    Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan 430070, China)

  • Jie Hu

    (Wuhan University of Technology, Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan 430070, China
    Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan 430070, China)

  • Richard Fiifi Turkson

    (Wuhan University of Technology, Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan 430070, China
    Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan 430070, China
    Mechanical Engineering Department, Ho Polytechnic, P. O. Box HP 217, Ho 036, Ghana)

  • Feng Lin

    (Wuhan University of Technology, Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan 430070, China
    Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan 430070, China)

Abstract

The objective of the study is to present the modeling and multi-objective optimization of NO x conversion efficiency and NH 3 slip in the Selective Catalytic Reduction (SCR) catalytic converter for a diesel engine. A novel ensemble method based on a support vector machine (SVM) and genetic algorithm (GA) is proposed to establish the models for the prediction of upstream and downstream NO x emissions and NH 3 slip. The data for modeling were collected from a steady-state diesel engine bench calibration test. After obtaining the two conflicting objective functions concerned in this study, the non-dominated sorting genetic algorithm (NSGA-II) was implemented to solve the multi-objective optimization problem of maximizing NO x conversion efficiency while minimizing NH 3 slip under certain operating points. The optimized SVM models showed great accuracy for the estimation of actual outputs with the Root Mean Squared Error (RMSE) of upstream and downstream NO x emissions and NH 3 slip being 44.01 × 10 −6 , 21.87 × 10 −6 and 2.22 × 10 −6 , respectively. The multi-objective optimization and subsequent decisions for optimal performance have also been presented.

Suggested Citation

  • Bo Liu & Fuwu Yan & Jie Hu & Richard Fiifi Turkson & Feng Lin, 2016. "Modeling and Multi-Objective Optimization of NO x Conversion Efficiency and NH 3 Slip for a Diesel Engine," Sustainability, MDPI, vol. 8(5), pages 1-13, May.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:5:p:478-:d:70212
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    References listed on IDEAS

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    1. d’Ambrosio, Stefano & Finesso, Roberto & Fu, Lezhong & Mittica, Antonio & Spessa, Ezio, 2014. "A control-oriented real-time semi-empirical model for the prediction of NOx emissions in diesel engines," Applied Energy, Elsevier, vol. 130(C), pages 265-279.
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    4. Asprion, Jonas & Chinellato, Oscar & Guzzella, Lino, 2013. "A fast and accurate physics-based model for the NOx emissions of Diesel engines," Applied Energy, Elsevier, vol. 103(C), pages 221-233.
    5. Lv, You & Liu, Jizhen & Yang, Tingting & Zeng, Deliang, 2013. "A novel least squares support vector machine ensemble model for NOx emission prediction of a coal-fired boiler," Energy, Elsevier, vol. 55(C), pages 319-329.
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    Cited by:

    1. Seongmin Kang & Joonyoung Roh & Eui-Chan Jeon, 2021. "Estimating the Characteristics and Emission Factor of Ammonia from Sewage Sludge Incinerator," IJERPH, MDPI, vol. 18(5), pages 1-7, March.
    2. Wei, Li & Yan, Fuwu & Hu, Jie & Xi, Guangwei & Liu, Bo & Zeng, Jiawei, 2017. "Nox conversion efficiency optimization based on NSGA-II and state-feedback nonlinear model predictive control of selective catalytic reduction system in diesel engine," Applied Energy, Elsevier, vol. 206(C), pages 959-971.

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