@inproceedings{cc9bf712ed87409db9f7e669fd5e7a01,
title = "A Review of Applications of Machine Learning for Emissions Estimation in Diesel Engines",
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{\textquoteright}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.",
keywords = "cylinder pressure, diesel engine, green house gases, machine learning, neural networks, virtual sensor",
author = "{Nguyen Khac}, Hoang and {Linh Nguyen}, Thuy",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; International Conference on Intelligent Systems and Networks, ICISN ; Conference date: 22-03-2024 Through 23-03-2024",
year = "2024",
doi = "10.1007/978-981-97-5504-2_75",
language = "English",
isbn = "9789819755035",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer",
pages = "651--657",
editor = "Nguyen, {Thi Dieu Linh} and Maurice Dawson and Ngoc, {Le Anh} and Lam, {Kwok Yan}",
booktitle = "Proceedings of the International Conference on Intelligent Systems and Networks - ICISN 2024",
address = "Germany",
}