A Review of Applications of Machine Learning for Emissions Estimation in Diesel Engines

Hoang Nguyen Khac*, Thuy Linh Nguyen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

Abstract

There has been an increasing demand to reduce the emissions of diesel engines, especially in maritime applications. Moreover, emission regulations are becoming stricter every year. This has led to an urge for more complex engine control systems with more accurate emissions estimators included. Machine learning methods have been long adopted to create models with high complexity to estimate the engine’s emissions and to rely less on conventional physical measurement devices. This paper presents a brief review of the development of engine emissions estimation using machine learning methods over the last 20 years. The review will however mainly focus on emissions prediction from engine in-cylinder pressure and engine functional vibration signal.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Intelligent Systems and Networks - ICISN 2024
EditorsThi Dieu Linh Nguyen, Maurice Dawson, Le Anh Ngoc, Kwok Yan Lam
PublisherSpringer
Pages651-657
Number of pages7
ISBN (Print)9789819755035
DOIs
Publication statusPublished - 2024
MoE publication typeA4 Conference publication
EventInternational Conference on Intelligent Systems and Networks - Hanoi, Viet Nam
Duration: 22 Mar 202423 Mar 2024

Publication series

NameLecture Notes in Networks and Systems
Volume1077 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

ConferenceInternational Conference on Intelligent Systems and Networks
Abbreviated titleICISN
Country/TerritoryViet Nam
CityHanoi
Period22/03/202423/03/2024

Keywords

  • cylinder pressure
  • diesel engine
  • green house gases
  • machine learning
  • neural networks
  • virtual sensor

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