Data-driven prediction of flame temperature and pollutant emission in distributed combustion

•Machine learning modeling was investigated in lean distributed combustion regime.•Flame temperature and pollutants were predicted using artificial neural network.•Effect of different learning rate on model training was studied.•Current model showed very well prediction in swirl combustion and distr...

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Vydáno v:Applied energy Ročník 310; s. 118502
Hlavní autoři: Roy, Rishi, Gupta, Ashwani K.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 15.03.2022
Témata:
air flow
algorithms
Artificial neural network
Bayesian theory
carbon dioxide
combustion
data collection
Distributed combustion
Emission and flame temperature prediction
energy
Learning rate
methane
neural networks
pollutants
prediction
Swirl burner
temperature
Artificial neural network
Emission and flame temperature prediction
Swirl burner
Learning rate
Distributed combustion
ISSN:0306-2619, 1872-9118
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Shrnutí:•Machine learning modeling was investigated in lean distributed combustion regime.•Flame temperature and pollutants were predicted using artificial neural network.•Effect of different learning rate on model training was studied.•Current model showed very well prediction in swirl combustion and distributed combustion.•Different backpropagation algorithms were investigated to optimize the model. The flame temperature and pollutant emission (of NO and CO) characteristics in distributed combustion were examined using data-driven artificial neural network (ANN) approach. Experimental results collected from swirl-assisted distributed combustion using methane fuel at an equivalence ratio 0.9 were utilized for dataset preparation. The distributed combustion condition was created in a swirl-assisted burner (at thermal intensity of 5.72 MW/m3-atm) by diluting the main airstream with carbon dioxide. Experimental results of exhaust NO and CO concentrations and adiabatic flame temperature (AFT) derived from Chemkin-Pro® simulation were selected as the target output. The ANN model was developed with inlet airflow rates and O2 concentrations as the input, and pollutant emission and AFT as the output variables. The ANN possessed one hidden layer with variable number of neurons (10, 15, and 20). Tangent sigmoid and Log sigmoid transfer functions were tested along with feed forwards and cascade forward network schemes having Levenberg–Marquardt backpropagation training algorithms. Different learning rates of model training were investigated to determine the optimized training model. The best model (with learning rate 0.2) was selected based on the mean square error (MSE) and the strength of correlation (R) predicted for individual outputs. The model predicted very well the target outputs in both conventional swirl combustion and distributed combustion regime for wider applicability in a range of applications. A very strong correlation was predicted by the current ANN model for the overall data as indicated by the R value of 0.9842. Furthermore, the results obtained with Bayesian Regularization backpropagation method demonstrated better prediction than those from Levenberg–Marquardt algorithm.
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ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2021.118502