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

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  • Roy, Rishi
  • Gupta, Ashwani K.

Abstract

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.

Suggested Citation

  • Roy, Rishi & Gupta, Ashwani K., 2022. "Data-driven prediction of flame temperature and pollutant emission in distributed combustion," Applied Energy, Elsevier, vol. 310(C).
    • Handle: RePEc:eee:appene:v:310:y:2022:i:c:s0306261921017177
    DOI: 10.1016/j.apenergy.2021.118502
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    References listed on IDEAS

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    1. Najafi, G. & Ghobadian, B. & Tavakoli, T. & Buttsworth, D.R. & Yusaf, T.F. & Faizollahnejad, M., 2009. "Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network," Applied Energy, Elsevier, vol. 86(5), pages 630-639, May.
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    5. Khalil, Ahmed E.E. & Gupta, Ashwani K., 2011. "Swirling distributed combustion for clean energy conversion in gas turbine applications," Applied Energy, Elsevier, vol. 88(11), pages 3685-3693.
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    Cited by:

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    2. Okeleye, Samuel Adeola & Thiruvengadam, Arvind & Perhinschi, Mario G. & Carder, Daniel, 2024. "Data-driven machine learning model of a Selective Catalytic Reduction on Filter (SCRF) in a heavy-duty diesel engine: A comparison of Artificial Neural Network with Tree-based algorithms," Energy, Elsevier, vol. 290(C).
    3. Yan, Peiliang & Fan, Weijun & Zhang, Rongchun, 2023. "Predicting the NOx emissions of low heat value gas rich-quench-lean combustor via three integrated learning algorithms with Bayesian optimization," Energy, Elsevier, vol. 273(C).

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