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 Programme
Overview
Detailed
Programme
| Neural
Networks in Process Engineering (NNPE) |
CHAIR: HENRIK SAXEN
Time: Tuesday, March
22nd, 14h20-16h00
| NNPE-1 |
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| Title: |
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Crack width prediction of RC structures
by Artificial Neural Networks |
| Author(s): |
Carlos Avila ,
Yukikazu Tsuji,
Yoichi Shiraishi |
| Abstract: |
This paper proposes the use of Artificial Neural
Networks (ANN) for the prediction of the maximum surface crack
width of precast reinforced concrete beams joined by steel
coupler connectors and anchor bars (jointed beams). Two different
training algorithms are used in this study and their performances
are compared. The first approach used Back propagation (BPANN)
and the second one includes Genetic Algorithms (GANN) during
the training process. Input and output vectors are designed
on the basis of empirical equations available in the literature
to estimate crack widths in common reinforced concrete (RC)
structures and parameters that characterize the mechanical
behavior of RC beams with overlapped reinforcement. Two well-defined
points of loading are considered in this study to demonstrate
the suitability of this approach in both, a linear and a highly
nonlinear stage of the mechanical response of this type of
structures. Remarkable results were obtained, however, in
all cases the combined Genetic Artificial Neural Network approach
resulted in improved prediction performance over networks
trained by error back propagation. |
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| NNPE-2 |
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| Title: |
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A neural network system for modelling
of coagulant dosage used in drinking water treatment |
| Author(s): |
B. Lamrini,
A. Benhammou,
A. Karama,
M-V. Le Lann |
| Abstract: |
This paper presents the elaboration and validation
of “software sensor” using neural networks for
on-line estimation of the coagulation dose from raw water
characteristics. The main parameters influencing the coagulant
dosage are firstly determined via a PCA. A brief description
of the methodology used for the synthesis of neural model
is given and experimental results are included. The training
of the neural network is performed using the Weight Decay
regularization in combination with Levenberg-Marquardt method.
The performance of this software sensor is illustrated with
real data. |
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| NNPE-3 |
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| Title: |
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ANN modeling applied to NOx reduction
with octane. Ann future in personal vehicles |
| Author(s): |
Mats Rönnholm,
Kalle Arve,
Kari Eränen,
F redrik Klingstedt,
Tapio Salmi,
Henrik Saxén,
Jan Westerholm |
| Abstract: |
A silver/alumina catalyst was tested for its
NOx reduction activity during oxygen-rich conditions and during
variation in the input parameters (nitric oxide, octane and
oxygen). The experimental data were investigated by means
of artificial neural networks. |
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| NNPE-4 |
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| Title: |
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A method for detecting cause-effects
in data from complex processes |
| Author(s): |
M. Helle,
H. Saxén |
| Abstract: |
When models are developed to aid the decision
making in the operation of industrial processes, a lack understanding
of the underlying mechanisms can make a first-principles modeling
approach infeasible. An alternative is to develop a black-box
model on the basis of historical data, and neural networks
can be used for this purpose to cope with nonlinearities.
Since numerous factors may influence the variables to be modeled,
and all potential inputs cannot be considered, one may instead
focus upon occasions where the (input or output) variables
exhibit larger changes. The paper describes a modeling method
by which historical data can be interpreted with respect to
changes in key variables, yielding a model that is well suited
for what-if analysis of how changes in the input variables
affect the outputs. |
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| NNPE-5 |
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| Title: |
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Predictive data mining on rubber
compound database |
| Author(s): |
M. Trebar,
U. Lotric |
| Abstract: |
Neural network based predictive data mining
techniques are used to find relationships between rubber compound
parameters obtained by rheological and mechanical tests. The
preprocessing methods appropriate to the problem are also
introduced. Good prediction of different rubber compound parameters
evidently indicate that the majority of rubber compounds'
mechanical properties can be devised from the rheological
measurements of cross-linking process. |
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