Predicting the NOx emissions of low heat value gas rich-quench-lean combustor via three integrated learning algorithms with Bayesian optimization

With the increased attention to low heat value gas fuels in recent years, research on NOx emissions from the combustors of low heat value gas fuels is necessary. This study introduces integrated learning algorithms to the NOx pollution emission prediction from gas turbine combustors. The combustor i...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Energy (Oxford) Ročník 273; s. 127227
Hlavní autori: Yan, Peiliang, Fan, Weijun, Zhang, Rongchun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 15.06.2023
Predmet:
air
air temperature
algorithms
Bayesian theory
energy
heat
Integrated learning algorithm
Low heat value gas
mass flow
NOx emission
pollutants
pollution
Prediction
RQL combustor
Integrated learning algorithm
NOx emission
Low heat value gas
RQL combustor
Prediction
ISSN:0360-5442
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:With the increased attention to low heat value gas fuels in recent years, research on NOx emissions from the combustors of low heat value gas fuels is necessary. This study introduces integrated learning algorithms to the NOx pollution emission prediction from gas turbine combustors. The combustor is a self-designed low heat value gas rich-quench-lean combustor and 92 sets of emission data have been obtained using simulation methods. The simulation data has been experimentally verified. The effect of four control parameters on NOx emissions has been investigated: inlet air temperature, inlet air mass flow rate, swirler installation angle, and combustor lean burn zone length. Pearson correlation coefficients show that four control parameters have very low correlations. The three integrated learning algorithms used in this study are the random forest regression algorithm, the extreme gradient boosting algorithm, and the natural gradient boosting algorithm. The hyperparameters of the three integrated learning algorithms are optimised using the Bayesian optimization algorithm. Three integrated learning algorithms are utilized to predict pollutant emission characteristics and the results show that NGboost provides the best predictions and Random Forest the worst, with NGboost suffering from overfitting problems. The results of the algorithm predictions were analyzed to understand the impact of different control parameters on the NOx emissions. A feature importance evaluation shows that the influence of inlet air temperature on NOx emissions far outweighs the other three parameters. •Random forest, XGboost, and NGboost for estimating NOx emissions of low heat value gas RQL combustor.•Accuracy Analysis of the developed models using statistical evaluation.•Analysis of the effect of different control parameters on NOx emissions.•NGboost surpasses in accuracy compared to the other algorithms.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0360-5442
DOI:10.1016/j.energy.2023.127227