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Machine learning based technique for outlier detection and result prediction in combustion diagnostics

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  • Chen, Mingfei
  • Zhou, Kaile
  • Liu, Dong

Abstract

With the utilization of data correlation, this work proposed a novel machine learning-based technique for outlier detection and result prediction to cover the deficiency and economize the cost of current combustion diagnostic technology. For outlier detection, the measurement results were performed through cluster analysis. Results demonstrated the measurement outlier could be successfully detected and eliminated by DBSCAN and the proposed GBCN algorithm. For result prediction, an artificial neural network (ANN) was established based on the correlation of the measured data, and its performance was investigated by the statistical method. Results indicated the ANN could well “learn” the distribution characteristic of flame temperature with a high value of R and a low value of MAPE by selecting Tanh and Sigmoid as activation functions. When there were 30 % and 50 % continuous unmeasured temperature data inside the flame, the ANN could still predict their values approximately based on the correlation between the remaining data. With the increase of prediction amount to 60 %, the prediction performance decreased significantly. However, even if there were 70 % random unmeasured temperature data inside the flame, the whole temperature field could still be obtained by the prediction of ANN with high accuracy based on the correlation between a small amount of measured data.

Suggested Citation

  • Chen, Mingfei & Zhou, Kaile & Liu, Dong, 2024. "Machine learning based technique for outlier detection and result prediction in combustion diagnostics," Energy, Elsevier, vol. 290(C).
    • Handle: RePEc:eee:energy:v:290:y:2024:i:c:s0360544223036125
    DOI: 10.1016/j.energy.2023.130218
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    References listed on IDEAS

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    1. Tornatore, Cinzia & Marchitto, Luca & Valentino, Gerardo & Esposito Corcione, Felice & Merola, Simona Silvia, 2012. "Optical diagnostics of the combustion process in a PFI SI boosted engine fueled with butanol–gasoline blend," Energy, Elsevier, vol. 45(1), pages 277-287.
    2. Zhang, Ji & Jing, Wei & Roberts, William L. & Fang, Tiegang, 2013. "Soot temperature and KL factor for biodiesel and diesel spray combustion in a constant volume combustion chamber," Applied Energy, Elsevier, vol. 107(C), pages 52-65.
    3. Can, Özer & Baklacioglu, Tolga & Özturk, Erkan & Turan, Onder, 2022. "Artificial neural networks modeling of combustion parameters for a diesel engine fueled with biodiesel fuel," Energy, Elsevier, vol. 247(C).
    4. Tian, Zhen & Gan, Wanlong & Zou, Xianzhi & Zhang, Yuan & Gao, Wenzhong, 2022. "Performance prediction of a cryogenic organic Rankine cycle based on back propagation neural network optimized by genetic algorithm," Energy, Elsevier, vol. 254(PB).
    5. Zhang, H.G. & Han, X.J. & Yao, B.F. & Li, G.X., 2013. "Study on the effect of engine operation parameters on cyclic combustion variations and correlation coefficient between the pressure-related parameters of a CNG engine," Applied Energy, Elsevier, vol. 104(C), pages 992-1002.
    6. Si, Jicang & Wang, Guochang & Li, Pengfei & Mi, Jianchun, 2021. "A new skeletal mechanism for simulating MILD combustion optimized using Artificial Neural Network," Energy, Elsevier, vol. 237(C).
    7. Ren, Tao & Modest, Michael F. & Fateev, Alexander & Sutton, Gavin & Zhao, Weijie & Rusu, Florin, 2019. "Machine learning applied to retrieval of temperature and concentration distributions from infrared emission measurements," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    8. Meng, Xiangmei & de Jong, Wiebren & Kudra, Tadeusz, 2016. "A state-of-the-art review of pulse combustion: Principles, modeling, applications and R&D issues," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 73-114.
    Full references (including those not matched with items on IDEAS)

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