A Random Forest Algorithmic Approach to Predicting Particulate Emissions from a Highly Boosted GDI Engine

Sam Akehurst, Andrew Lewis, James Turner, Felix C.P. Leach, Nick Papaioannou, XiaoHang Fang

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

6 Citations (SciVal)


Particulate emissions from gasoline direct injection (GDI) engines continue to be a topic of substantial research interest. Forthcoming regulation both in the USA and the EU will further reduce their emission and drive innovation. Substantial research effort is spent undertaking experiments to understand, characterize, and research particle number (PN) emissions from engines and vehicles. Recent advances in computing power, data storage, and understanding of artificial intelligence algorithms now mean that these are becoming an important tool in engine research. In this work a random forest (RF) algorithm is used for the prediction of PN emissions from a highly boosted (up to 32 bar BMEP) GDI engine. Particle size, concentration, and the accumulation mode geometric standard deviation (GSD) are all predicted by the model. The results are analysed and an in depth study on parameter importance is carried out. The Random Forest algorithm is used as an estimator and the various engine parameters are ranked with a permutation feature importance technique using mean squared error as a performance metric. The results showed that from 82 model parameters only 17 are important for predicting the above PN emission parameters. Moreover, the permutation importance algorithm showed that when the parameters are reduced to 9 the model accuracy is improved due to a reduction in model variance. Overall, the model shows excellent predictive performance for all three parameters even when an independent dataset is used.

Original languageEnglish
Title of host publicationSAE Naples ICE 2021 Confrerence
Place of PublicationU. S. A.
PublisherSAE International
Number of pages9
Publication statusPublished - 5 Sept 2021

Publication series

NameSAE Technical Papers

Bibliographical note

Funding Information:
The authors acknowledge the Technology Strategy Board (now Innovate UK), the UK's innovation agency, for the partial funding of this work. Consortium members GE Precision Engineering, Jaguar Land Rover, Shell, Lotus Engineering, CD-Adapco, Imperial College London, University of Bath and the University of Leeds have all made various portions of this work possible.

Publisher Copyright:
© 2021 SAE International. All Rights Reserved.

ASJC Scopus subject areas

  • Automotive Engineering
  • Safety, Risk, Reliability and Quality
  • Pollution
  • Industrial and Manufacturing Engineering


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