Diesel engine pollutant prediction and remote vehicle monitoring Part I

The prediction of diesel engine smoke emission using neural networks

Edward Berry, Peter Kukla, Fengshou Gu, Andrew D. Ball

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Accurate measurement of diesel engine exhaust smoke emission is a primary phase in meeting the everstricter European Union regulations on emission levels and a fundamental step towards the improvement of many factors including fuel economy, atmospheric pollution levels and more importantly, human health, with the additional aim of automatic engine managemeht systems and condition-based maintenance. However, it is difficult to measure continuously smoke levels directly and in real-time on a vehicle in transit due to the size and cost of the necessary equipment, therefore this paper (Part I) documents a study into the feasibility of diesel engine exhaust smoke prediction based upon a variety of engine operating parameters recorded from three different engines using neural network (NN) models. In this paper two types of NN have been investigated and optimised to develop a prediction. The results show that smoke levels can be predicted by means of indirect measurements with good accuracy. Part II of this paper describes how the NN model is used with real-time data collected remotely from a vehicle on the road to predict smoke emission levels and introduces a method of mapping these smoke levels on a city map at street level via the Internet.

Original languageEnglish
Pages (from-to)24-30
Number of pages7
JournalInternational Journal of COMADEM
Volume7
Issue number4
Publication statusPublished - 1 Oct 2004
Externally publishedYes

Fingerprint

Smoke
Diesel engines
Neural networks
Monitoring
Vehicle Emissions
Exhaust systems (engine)
Engines
Fuel economy
Prediction
Diesel engine
Pollution
Health
Internet
Network model
Costs

Cite this

@article{f33fa06234484af99774be55437de1c8,
title = "Diesel engine pollutant prediction and remote vehicle monitoring Part I: The prediction of diesel engine smoke emission using neural networks",
abstract = "Accurate measurement of diesel engine exhaust smoke emission is a primary phase in meeting the everstricter European Union regulations on emission levels and a fundamental step towards the improvement of many factors including fuel economy, atmospheric pollution levels and more importantly, human health, with the additional aim of automatic engine managemeht systems and condition-based maintenance. However, it is difficult to measure continuously smoke levels directly and in real-time on a vehicle in transit due to the size and cost of the necessary equipment, therefore this paper (Part I) documents a study into the feasibility of diesel engine exhaust smoke prediction based upon a variety of engine operating parameters recorded from three different engines using neural network (NN) models. In this paper two types of NN have been investigated and optimised to develop a prediction. The results show that smoke levels can be predicted by means of indirect measurements with good accuracy. Part II of this paper describes how the NN model is used with real-time data collected remotely from a vehicle on the road to predict smoke emission levels and introduces a method of mapping these smoke levels on a city map at street level via the Internet.",
author = "Edward Berry and Peter Kukla and Fengshou Gu and Ball, {Andrew D.}",
year = "2004",
month = "10",
day = "1",
language = "English",
volume = "7",
pages = "24--30",
journal = "International Journal of COMADEM",
issn = "1363-7681",
publisher = "COMADEM International",
number = "4",

}

TY - JOUR

T1 - Diesel engine pollutant prediction and remote vehicle monitoring Part I

T2 - The prediction of diesel engine smoke emission using neural networks

AU - Berry, Edward

AU - Kukla, Peter

AU - Gu, Fengshou

AU - Ball, Andrew D.

PY - 2004/10/1

Y1 - 2004/10/1

N2 - Accurate measurement of diesel engine exhaust smoke emission is a primary phase in meeting the everstricter European Union regulations on emission levels and a fundamental step towards the improvement of many factors including fuel economy, atmospheric pollution levels and more importantly, human health, with the additional aim of automatic engine managemeht systems and condition-based maintenance. However, it is difficult to measure continuously smoke levels directly and in real-time on a vehicle in transit due to the size and cost of the necessary equipment, therefore this paper (Part I) documents a study into the feasibility of diesel engine exhaust smoke prediction based upon a variety of engine operating parameters recorded from three different engines using neural network (NN) models. In this paper two types of NN have been investigated and optimised to develop a prediction. The results show that smoke levels can be predicted by means of indirect measurements with good accuracy. Part II of this paper describes how the NN model is used with real-time data collected remotely from a vehicle on the road to predict smoke emission levels and introduces a method of mapping these smoke levels on a city map at street level via the Internet.

AB - Accurate measurement of diesel engine exhaust smoke emission is a primary phase in meeting the everstricter European Union regulations on emission levels and a fundamental step towards the improvement of many factors including fuel economy, atmospheric pollution levels and more importantly, human health, with the additional aim of automatic engine managemeht systems and condition-based maintenance. However, it is difficult to measure continuously smoke levels directly and in real-time on a vehicle in transit due to the size and cost of the necessary equipment, therefore this paper (Part I) documents a study into the feasibility of diesel engine exhaust smoke prediction based upon a variety of engine operating parameters recorded from three different engines using neural network (NN) models. In this paper two types of NN have been investigated and optimised to develop a prediction. The results show that smoke levels can be predicted by means of indirect measurements with good accuracy. Part II of this paper describes how the NN model is used with real-time data collected remotely from a vehicle on the road to predict smoke emission levels and introduces a method of mapping these smoke levels on a city map at street level via the Internet.

UR - http://www.scopus.com/inward/record.url?scp=10244239465&partnerID=8YFLogxK

M3 - Article

VL - 7

SP - 24

EP - 30

JO - International Journal of COMADEM

JF - International Journal of COMADEM

SN - 1363-7681

IS - 4

ER -