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Study of predictive models for emissions in hydrogen assisted dual fuel internal combustion engine

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Nguyen, Viet Tien Phuong 2007 , 'Study of predictive models for emissions in hydrogen assisted dual fuel internal combustion engine', PhD thesis, University of Tasmania.

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Abstract

A detailed understanding of the exhaust gas emissions can forecast the state of the engine
performance and the other detrimental health effects it can have on the general population.
There is no doubt that exhaust gas emissions generated by various internal combustion engines
can provide environmental implications. With modem trends in alternative fuels and their mix
with the conventional gasoline, is yet another effort to reduce exhaust gas emissions and adhere
to strict emissions requirements in automobiles. While a good understanding of the quantitative
and qualitative trends are available in the literature, for petrol driven vehicles, little or no
published evidence is available for hydrogen vehicles or dual fuel driven vehicles assisted with
hydrogen. A good understanding of the near zero emissions and associated conversion
technology, using hydrogen as fuel, has been in the domain of few automotive companies
around the world. While hydrogen is recognised as a potential fuel of the future, little or no
evidence is available in the public domain on the mechanical and electrical conversion
technologies and associated emission data for better understanding of this emerging alternative
fuel. Conventional engine management systems with their inherent ability to map a particular
fuel needed to be modified with dual fuel injection and particular add-on modular tools to
accommodate hydrogen injection.
This work is aimed at converting a commercially available Kawasaki Ninja 600cc motorcycle
engine to run on both hydrogen and petrol. In this thesis, a rigorous design for conversion to
run on hydrogen is designed and built from first principles. The test rig development associated
with the calculations for fuel flow rates and associated engine management systems are integral
part of this overall systematic design. As part of this investigation, an innovative fuel injection
system together with add-on injection system is developed. Using artificial neural networks,
predictive models for various mixtures of hydrogen-petrol are developed to estimate emissions
for various hydrogen-petrol mixtures. It is argued in this thesis that the accuracy of prediction
for emissions can replace expensive gas emissions equipment so that the intelligent
mathematical predictive tools can be used as virtual sensors. As part of this investigation a
comprehensive range of engine operating conditions is tested using both petrol and hydrogen
as fuel for various combinations. The predictive model as virtual sensors has shown that the
predictive capability for emissions is close to ±10% for various combinations of hydrogen
petrol fuel mix. Exhaust emission performance showed significant reduction in oxides of
nitrogen and no significant emissions of hydrocarbons, carbon dioxide and carbon monoxide
with increasing -percentages of hydrogen injection. This work is seen a step towards
understanding the intricate hydrogen conversions, development of add-on electronic injection
control units to accommodate hydrogen and neural network based predictive models, as virtual
sensors, to estimate internal combustion performance.

Item Type: Thesis - PhD
Authors/Creators:Nguyen, Viet Tien Phuong
Keywords: Alternative fuel vehicles, Hydrogen as fuel, Motor fuels, Internal combustion engines
Copyright Holders: The Author
Copyright Information:

Copyright 2007 the author

Additional Information:

Available for library use only but NOT for copying until 30 November 2009. After that date, available for use in the Library and copying in accordance with the Copyright Act 1968, as amended. Thesis (PhD)--University of Tasmania, 2007. Includes bibliographical references. Ch. 1. Introduction -- Ch. 2. Literature survey -- Ch. 3. Artificial neural network as predictive models for various non-linear dynamic processes -- Ch. 4. Experimental test rig set-up and calibration procedures -- Ch. 5. Embedded add-on fuel injection system with virtual emission sensor -- Ch. 6. Predictive models for emissions in petrol-hydrogen dual fuel engine using neural network -- Ch. 7. Online appraisal of the system -- Ch. 8. Final concluding remarks and proposed future works

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