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Heat Exchanger Fouling and Cleaning VII
July 1-6, 2007 - Tomar, Portugal
| Editors: |
Hans Müller-Steinhagen, Institute of Technical Thermodynamics, German Aerospace Centre (DLR)
and Institute for Thermodynamics and Thermal Engineering, University of Stuttgart, Germany
M. Reza Malayeri, University of Stuttgart, Germany
A. Paul Watkinson, The University of British Columbia, Canada |
The articles for these proceedings are peer-reviewed.
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INTELLIGENT DISCRIMINATION MODEL TO IDENTIFY INFLUENTIAL PARAMETERS DURING CRYSTALLISATION FOULING
M.R. Malayeri, Institute for Thermodynamics and Thermal Engineering (ITW), University of Stuttgart, Pfaffenwaldring 6, D-70550, Stuttgart, Germany
H. Müller-Steinhagen, Institute for Thermodynamics and Thermal Engineering (ITW), University of Stuttgart Pfaffenwaldring 6, D-70550, Stuttgart, Germany & Institute for Technical Thermodynamics, German Aerospace Centre (DLR), Pfaffenwaldring 38-40, D-70569, Stuttgart, Germany
ABSTRACT:
The introduction of redundant independent variables
into any function approximation model, or the neglect of
important variables, may result in a correlation with poor
prediction and reduced reliability. This paper demonstrates
that a novel integrated model of neural networks and
genetic algorithms can deal with this problem robustly with
good accuracy, while be far less time-consuming compared
to lengthy conventional models Furthermore, a redundant
variable input was imposed to the model to discern if the
approach could identify it among other important variables.
Genetic algorithms were exploited as a powerful
optimisation tool for the selection of best set of inputs with
the help of process “prior knowledge” rules.
A comprehensive databank of crystallisation fouling
under subcooled flow boiling was used. The resulting model
was capable of handling the data and successfully
discriminated among several independent inputs if there is
any redundant input. The technique may be regarded as a
robust method to prevent data over-fitting as well as
processes where a large number of inputs are involved such
as crude oil fouling.
M.R. Malayeri and H. Müller-Steinhagen, "INTELLIGENT DISCRIMINATION MODEL TO IDENTIFY INFLUENTIAL PARAMETERS DURING CRYSTALLISATION FOULING" in "Heat Exchanger Fouling and Cleaning VII", Hans Müller-Steinhagen, Institute of Technical Thermodynamics, German Aerospace Centre (DLR)
and Institute for Thermodynamics and Thermal Engineering, University of Stuttgart, Germany
M. Reza Malayeri, University of Stuttgart, Germany
A. Paul Watkinson, The University of British Columbia, Canada
Eds, ECI
Symposium Series, Volume RP5 (2007). http://services.bepress.com/eci/heatexchanger2007/37
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