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.
| Language | English |
|---|---|
| Pages | 24-30 |
| Number of pages | 7 |
| Journal | International Journal of COMADEM |
| Volume | 7 |
| Issue number | 4 |
| Publication status | Published - 1 Oct 2004 |
| Externally published | Yes |
Fingerprint
Cite this
}
Diesel engine pollutant prediction and remote vehicle monitoring Part I : The prediction of diesel engine smoke emission using neural networks. / Berry, Edward; Kukla, Peter; Gu, Fengshou; Ball, Andrew D.
In: International Journal of COMADEM, Vol. 7, No. 4, 01.10.2004, p. 24-30.Research output: Contribution to journal › Article
TY - JOUR
T1 - Diesel engine pollutant prediction and remote vehicle monitoring Part I
T2 - International Journal of COMADEM
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 -