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Comparative research on DNN and LSTM algorithms for soot emission prediction under transient conditions in a diesel engine

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Abstract

Deep learning approaches were applied to predict soot emissions under transient conditions in a diesel engine using the worldwide harmonized light vehicles test procedure (WLTP) cycles. The accuracies of deep neural networks (DNN) and long short-term memory (LSTM) models were compared to predict emissions. The accuracy of the LSTM model had an R2 value of 0.9761, which was higher than that of the DNN model, with an R2 value of 0.9215. The mean absolute errors (MAEs) of the WLTP cycles predicted by the LSTM model were between 0.30 %–1.47 % compared with the maximum measured values in WLTP cycles. For local prediction, the LSTM model followed the fluctuations in the data and local peak values well. However, the calculation time of the LSTM model is longer than that of the DNN model. Researchers should consider the purpose of the model based on the accuracy-time trade-off relationship between DNN and LSTM models.

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Abbreviations

ANN :

Artificial neural network

BSFC :

Brake-specific fuel consumption

CFD :

Computational fluid dynamics

DNN :

Deep neural networks

DPF :

Diesel particulate filter

ECU :

Engine control unit

ELU :

Exponential linear unit

LSTM :

Long short-term memory

MAE :

Mean absolute error

NO x :

Nitrogen oxides

RDE :

Real driving emissions

RMSE :

Root mean squared error

RNN :

Recurrent neural network

SOF :

Soluble organic fraction

STD :

Standard deviation

WLTP :

Worldwide harmonized light vehicle test procedure

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Acknowledgments

This study was supported by Sejong University and Korea Railway Research Institute (PK2201C1). The results of this study were also supported by “HPC Support” Project, organized by ‘Ministry of Science and ICT’ and NIPA.

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Authors and Affiliations

  1. Department of Artificial Intelligence, Sejong University, Seoul, 05006, Korea

    Seunghyup Shin

  2. Artificial Intelligence Railroad Research Department, Korea Railroad Research Institute, Uiwang-si, 16105, Korea

    Jong-Un Won & Minjeong Kim

Authors
  1. Seunghyup Shin
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  2. Jong-Un Won
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  3. Minjeong Kim
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Corresponding author

Correspondence to Minjeong Kim.

Additional information

Seunghyup Shin obtained his B.S. (2009), M.S. (2011) and Ph.D. (2021) in Department of Mechanical and Aerospace Engineering from Seoul National University. He is currently an Assistant Professor in Department of Artificial Intelligence at Sejong University, Seoul, Korea.

Minjeong Kim obtained her B.S. (2010), M.S. (2012) and Ph.D. (2016) in Department of Environmental Engineering from KyungHee University, Korea. She is currently a Senior Researcher in Artificial Intelligence Railroad Research Department at Korea Railroad Research Institute (KRRI).

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Shin, S., Won, JU. & Kim, M. Comparative research on DNN and LSTM algorithms for soot emission prediction under transient conditions in a diesel engine. J Mech Sci Technol 37, 3141–3150 (2023). https://doi.org/10.1007/s12206-023-0538-y

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  • DOI: https://doi.org/10.1007/s12206-023-0538-y

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