Machine Learning Methods for Emissions Prediction in Combustion Engines with Multiple Cylinders

Hoang Nguyen Khac*, Amin Modabberian, Kai Zenger, Kalle Niskanen, Anton West , Yejun Zhang, Elias Silvola, Eric Lendormy, Xiaoguo Storm, Maciej Mikulski

*Corresponding author for this work

Research output: Contribution to journalConference articleScientificpeer-review

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Abstract

The increasing demand of lowering the emissions of the combustion engines has led to the development of more complex engine systems. This paper presents artificial neural network (ANN) based models for estimating nitrogen oxide (NOx) and carbon dioxide (CO2) emissions from in-cylinder pressure of a maritime diesel engine. The architecture of the models is that of Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) network. The data utilized to train and test the models are obtained from a four-cylinder marine engine. The inputs of the models are chosen as the first principal components of the in-cylinder pressure and engine parameters with highest correlation to aforementioned greenhouse gases. Generalization is performed on the models during the training to avoid overfitting. The estimation result of each model is then compared. Additionally, contribution of each cylinder to the production of emissions is investigated. Results indicate that MLP has a higher accuracy in estimating both NOx and CO2 compared to RBF network. The emission levels of each cylinder for both NOx and CO2 are mostly even due to the nature of the conventional diesel engine.
Original languageEnglish
Pages (from-to)3072-3078
Number of pages6
JournalIFAC-PapersOnLine
Volume56
Issue number2
DOIs
Publication statusPublished - 2023
MoE publication typeA4 Conference publication
EventIFAC World Congress - Yokohama, Japan
Duration: 9 Jul 202314 Jul 2023
Conference number: 22

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  • CPT Zenger: Clean Propulsion Technologies

    Zenger, K. (Principal investigator)

    01/02/202131/01/2023

    Project: Business Finland: Strategic centres for science, technology and innovation (SHOK)

  • -: CPT Zenger

    Zenger, K. (Principal investigator), Nguyen Khac, H. (Project Member) & Modabberian, A. (Project Member)

    01/02/202131/12/2023

    Project: Business Finland: Strategic centres for science, technology and innovation (SHOK)

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