Predicting emissions using an on-road vehicle performance simulator.
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Date
2002
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Abstract
South Africa is coming under increasing pressure to conform to the rest of the world in
terms of emissions regulations. The pressure is caused by a number of factors:
international organisations requiring local companies to adhere to environmental
conservation policies, evidence from within South Africa that efforts are being made to
reduce environmental pollution in line with other countries and keeping abreast of the
latest technologies that have been incorporated into vehicles to reduce emissions.
In light of these problems associated with emiSSions, a study was initiated by the
Department of Transport and the School of Bioresources Engineering and
Environmental Hydrology at the University of Natal to investigate and develop a
method of predicting emissions from a diesel engine. The main objective of this
research was to incorporate this model into SimTrans in order to estimate emissions
generated from a vehicle while it is travelling along specific routes in South Africa.
SimTrans is a mechanistically based model, developed at the School, that simulates a
vehicle travelling along a route, requiring input for the road profile and vehicle and
engine specifications.
After a preliminary investigation it was decided to use a neural network to predict
emissions, as it provides accurate results and is more suitable for a quantitative analysis
which is what was required for this study. The emissions that were predicted were NOx
(Nitric oxide-NO and Nitric dioxide-N02), CO (carbon monoxide), HC (unbumt
hydrocarbons) and particulates. The neural netWork was trained on emissions data
obtained from an ADE 447Ti engine. These neural networks were then integrated into
the existing SimTrans. Apart from the neural network, an algorithm to consider the
effect of ambient conditions on the output of the engine was also included in the model.
A sensitivity analysis was carried out using the model to prioritise the factors affecting
emissions. Finally using the data for the ADE 447Ti engine, a trip with a Mercedes
Benz 2644S-24 was simulated using different scenarios over the routes from Durban to
Johannesburg and Cape Town to Johannesburg in South Africa to quantify the
emissions that were generated.
Description
Thesis (M.Sc.)-University of Natal, Pietermaritzburg, 2002.
Keywords
Motor vehicles--Pollution control devices., Diesel motor exhaust gas--Environmental aspects., Diesel fuels--Environmental aspects., Air--Pollution--Environmental aspects., Neural networks (Computer Science), Diesel motor--Pollution control devices., Theses--Bioresources engineering and environmental hydrology.