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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| Published in: | Applied energy Vol. 310; p. 118502 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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15.03.2022
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| ISSN: | 0306-2619, 1872-9118 |
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| Abstract | •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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| AbstractList | •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. 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. |
| ArticleNumber | 118502 |
| Author | Gupta, Ashwani K. Roy, Rishi |
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| Cites_doi | 10.1016/j.apenergy.2011.03.048 10.1016/j.combustflame.2008.06.010 10.1007/s00348-021-03168-w 10.1016/j.applthermaleng.2015.01.057 10.2514/6.2017-1060 10.1007/s00521-016-2755-0 10.1016/j.fuel.2021.120356 10.1109/IJCNN.2002.1007668 10.1016/j.apenergy.2008.09.017 10.1007/s12665-017-7064-0 10.1016/j.fuel.2019.116460 10.1016/j.apenergy.2017.02.030 10.1115/GT2014-25030 10.1016/j.pecs.2009.01.002 10.1016/j.applthermaleng.2016.10.042 10.1016/j.heliyon.2020.e05511 10.1016/j.fuel.2016.06.098 10.1016/j.energy.2013.08.027 10.1016/j.proci.2020.06.135 10.1115/GT2008-51261 10.1016/j.apenergy.2013.11.078 10.1162/neco.1992.4.3.415 |
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| Keywords | Artificial neural network Emission and flame temperature prediction Swirl burner Learning rate Distributed combustion |
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Therm Eng doi: 10.1016/j.applthermaleng.2016.10.042 – volume: 6 start-page: e05511 issue: 11 year: 2020 ident: 10.1016/j.apenergy.2021.118502_b0040 article-title: Analysis of deep learning neural network combined with experiments to develop predictive models for a propane vertical jet fire publication-title: Heliyon doi: 10.1016/j.heliyon.2020.e05511 – volume: 184 start-page: 1 year: 2016 ident: 10.1016/j.apenergy.2021.118502_b0090 article-title: Impact of confinement on flowfield of swirl flow burners publication-title: Fuel doi: 10.1016/j.fuel.2016.06.098 – volume: 61 start-page: 606 year: 2013 ident: 10.1016/j.apenergy.2021.118502_b0085 article-title: Artificial neural network prediction of exhaust emissions and flame temperature in LPG (liquefied petroleum gas) fueled low swirl burner publication-title: Energy doi: 10.1016/j.energy.2013.08.027 – volume: 38 start-page: 3371 issue: 2 year: 2021 ident: 10.1016/j.apenergy.2021.118502_b0050 article-title: Conditional scalar dissipation 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10.1162/neco.1992.4.3.415 – ident: 10.1016/j.apenergy.2021.118502_b0120 |
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| Snippet | •Machine learning modeling was investigated in lean distributed combustion regime.•Flame temperature and pollutants were predicted using artificial neural... The flame temperature and pollutant emission (of NO and CO) characteristics in distributed combustion were examined using data-driven artificial neural network... |
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| SubjectTerms | 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 |
| Title | Data-driven prediction of flame temperature and pollutant emission in distributed combustion |
| URI | https://dx.doi.org/10.1016/j.apenergy.2021.118502 https://www.proquest.com/docview/2636468197 |
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