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A Long Short-Term Memory Neural Network for the Low-Cost Prediction of Soot Concentration in a Time-Dependent Flame

Author

Listed:
  • Mehdi Jadidi

    (Department of Mechanical and Industrial Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada)

  • Luke Di Liddo

    (Department of Mechanical and Industrial Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada)

  • Seth B. Dworkin

    (Department of Mechanical and Industrial Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada)

Abstract

Particulate matter (soot) emissions from combustion processes have damaging health and environmental effects. Numerical techniques with varying levels of accuracy and computational time have been developed to model soot formation in flames. High-fidelity soot models come with a significant computational cost and as a result, accurate soot modelling becomes numerically prohibitive for simulations of industrial combustion devices. In the present study, an accurate and computationally inexpensive soot-estimating tool has been developed using a long short-term memory (LSTM) neural network. The LSTM network is used to estimate the soot volume fraction ( f v ) in a time-varying, laminar, ethylene/air coflow diffusion flame with 20 Hz periodic fluctuation on the fuel velocity and a 50% amplitude of modulation. The LSTM neural network is trained using data from CFD, where the network inputs are gas properties that are known to impact soot formation (such as temperature) and the network output is f v . The LSTM is shown to give accurate estimations of f v , achieving an average error (relative to CFD) in the peak f v of approximately 30% for the training data and 22% for the test data, all in a computational time that is orders-of-magnitude less than that of high-fidelity CFD modelling. The neural network approach shows great potential to be applied in industrial applications because it can accurately estimate the soot characteristics without the need to solve the soot-related terms and equations.

Suggested Citation

  • Mehdi Jadidi & Luke Di Liddo & Seth B. Dworkin, 2021. "A Long Short-Term Memory Neural Network for the Low-Cost Prediction of Soot Concentration in a Time-Dependent Flame," Energies, MDPI, vol. 14(5), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1394-:d:509856
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    References listed on IDEAS

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    1. Mehdi Jadidi & Stevan Kostic & Leonardo Zimmer & Seth B. Dworkin, 2020. "An Artificial Neural Network for the Low-Cost Prediction of Soot Emissions," Energies, MDPI, vol. 13(18), pages 1-27, September.
    2. Yang, Guotian & Wang, Yingnan & Li, Xinli, 2020. "Prediction of the NOx emissions from thermal power plant using long-short term memory neural network," Energy, Elsevier, vol. 192(C).
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