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An EGR performance evaluation and decision-making approach based on grey theory and grey entropy analysis

Author

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  • Xianghuan Zu
  • Chuanlei Yang
  • Hechun Wang
  • Yinyan Wang

Abstract

Exhaust gas recirculation (EGR) is one of the main methods of reducing NOX emissions and has been widely used in marine diesel engines. This paper proposes an optimized comprehensive assessment method based on multi-objective grey situation decision theory, grey relation theory and grey entropy analysis to evaluate the performance and optimize rate determination of EGR, which currently lack clear theoretical guidance. First, multi-objective grey situation decision theory is used to establish the initial decision-making model according to the main EGR parameters. The optimal compromise between diesel engine combustion and emission performance is transformed into a decision-making target weight problem. After establishing the initial model and considering the characteristics of EGR under different conditions, an optimized target weight algorithm based on grey relation theory and grey entropy analysis is applied to generate the comprehensive evaluation and decision-making model. Finally, the proposed method is successfully applied to a TBD234V12 turbocharged diesel engine, and the results clearly illustrate the feasibility of the proposed method for providing theoretical support and a reference for further EGR optimization.

Suggested Citation

  • Xianghuan Zu & Chuanlei Yang & Hechun Wang & Yinyan Wang, 2018. "An EGR performance evaluation and decision-making approach based on grey theory and grey entropy analysis," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-15, January.
    • Handle: RePEc:plo:pone00:0191626
    DOI: 10.1371/journal.pone.0191626
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    References listed on IDEAS

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    1. Jafarmadar, Samad & Nemati, Peyman, 2017. "Analysis of Exhaust Gas Recirculation (EGR) effects on exergy terms in an engine operating with diesel oil and hydrogen," Energy, Elsevier, vol. 126(C), pages 746-755.
    2. Asad, Usman & Zheng, Ming, 2014. "Exhaust gas recirculation for advanced diesel combustion cycles," Applied Energy, Elsevier, vol. 123(C), pages 242-252.
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    1. Syed Mithun Ali & Amanat Ur Rahman & Golam Kabir & Sanjoy Kumar Paul, 2024. "Artificial Intelligence Approach to Predict Supply Chain Performance: Implications for Sustainability," Sustainability, MDPI, vol. 16(6), pages 1-31, March.

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