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2024 | OriginalPaper | Buchkapitel

Prediction of Emission Characteristics of Spark Ignition (S.I.) Engines with Premium Level Gasoline-Ethanol-Alkane Blends Using Machine Learning

verfasst von : Sujit Kumbhar, Sanjay Khot, Varsha Jujare, Vishal Patil, Avesahemad Husainy, Koustubha Shedbalkar

Erschienen in: Advanced Computing

Verlag: Springer Nature Switzerland

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Abstract

In the current research work, a single cylinder spark (S.I)ignition engine were used for investigations of premium level gasoline-ethanol-alkane experimentally with different operating conditions e.g. variation spark ignition timing. The Engine Lab and PE3 software were used for engine control and data acquisition system. The data obtained after experimentation were used to predict the engine emissions for different operating conditions. The engine emission characteristics were predicted using three machine learning algorithmsviz linear regression, decision tree and random forest. It was found that emissions characteristics such as carbon monoxide, unburnt hydrocarbon found to be minimum for 24°bTDC experimentally as well as predicted by machine learning algorithms with different operating conditions than other spark timing positions such as 15°, 18°, 21°, 27°, 30° bTDC. All three machine learning algorithms gave better results but the random forest algorithm were more accurate than linear regression and decision trees.

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Titel: Prediction of Emission Characteristics of Spark Ignition (S.I.) Engines with Premium Level Gasoline-Ethanol-Alkane Blends Using Machine Learning
verfasst von: Sujit Kumbhar
Sanjay Khot
Varsha Jujare
Vishal Patil
Avesahemad Husainy
Koustubha Shedbalkar
Verlag: Springer Nature Switzerland
Buch: Advanced Computing
Print ISBN: 978-3-031-56699-8

Electronic ISBN: 978-3-031-56700-1

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-56700-1_13

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