Computational intelligence approach for NOx emissions minimization in a 30 MW premixed gas burner

• Autorzy: Chen Weibo , Liu Guixiong
• Języki publikacji: EN

Abstrakty

EN

Artificial intelligence algorithms have become a research hotspot in attempts to reduce NOx emissions in gas burners through NOx emission modeling and optimizing operating parameters. This paper compres the predictive accuracy of NOx emission models based on LSSVM, SVR and ELM. CGA and three other GA based hybrid algorithms proposed to modify CGA were employed to optimize the operating parameters of a 30MW gas burner in order to reduce NOx emission. The results show that the NOx emission model built by LSSVM is more accurate than that of SVR and ELM. The mean relative error and correlation coefficient obtained by the LSSVM model were 0.0731% and 0.999, respectively. Among the four optimization algorithms, the novel TSGA proposed in this paper showed its superiority over the other three algorithms, excelling in its global searching ability and stability. The LSSVM plus TSGA method is a potential combination for predicting and reducing NOx emission by optimizing the operating parameters for the gas burner on-line.

Słowa kluczowe

EN

NOx emission modeling; operating parameters optimization; TSGA; hybrid algorithm; gas burner

PL

modelowanie emisji NOx; optymalizacja parametrów pracy; algorytm hybrydowy; palnik gazowy

• Wydawca: Institute of Heat Engineering, Warsaw University of Technology
• Czasopismo: Journal of Power Technologies
• Rocznik: 2020
• Tom: Vol. 100, nr 1
• Strony: 21--31
• Opis fizyczny: Bibliogr. 33 poz., rys., wykr.

Twórcy

autor

Chen Weibo

• School of Mechanical Automotive Engineering, South China University of Technology

autor

Liu Guixiong

• School of Mechanical Automotive Engineering, South China University of Technology

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).