Neural Network Models For The Development
And Evaluation Of New Fuels
Author(s)
G. S. Terra, A. G. Evsukoff, N. F. F. Ebecken, R. A. B. Sá
& R. M. C. F. Silva
Abstract
In this paper data mining techniques are used to model the non-trivial
relationships between dependent variables (mass exhaust emissions) and
independent variables (engines’ technologies and fuel properties). Models based
on experiments to predict pollutant emissions from gasoline properties and
engine technologies can improve the design process. An applied adaptive neural
network model structure provides for efficient combining of available expert
knowledge with existing experimental data. Efficient learning methods for neural
network were applied, and genetic optimization of the models was also explored.
Keywords: data mining, fuels technology, mass exhaust emission.
1 Introduction
Automobile industries are stimulated to develop efficient vehicles, with new
technologies that improve the autonomy and reduce the pollutant emissions.
Downstream oil companies also contribute when they develop new fuels that
provide a positive interaction with those new vehicle technologies.
The fuels’ characteristics are modified when some streams and additives are
incorporated in their formulation or changes are made in the proportion of their
basic composition. During the fuel formulation phase, different combinations are
tested in vehicles and theirs emissions are measured in controlled tests.
In the recent study developed by PETROBRAS Research and Development
Center – CENPES, and presented in 2004 Powertrain & Fluids System
Conference [1], models were built based on multiple linear regression
Keywords
data mining, fuels technology, mass exhaust emission.
Related Book
Other papers in this volume
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