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Digital Library of the
European Council for Modelling and Simulation |
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Title: |
Model Reduction Using Neural Networks Applied To The Modeling
Of Integrated Urban Wastewater Systems |
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Authors: |
Botond Ráduly, Krist V. Gernaey, Erik
Lindblom, Andrea G. Capodaglio |
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Published in:
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ECMS
2006 Proceedings This
text is replaced by thecitation reference from Crossref for the whole
proceedings (including doi-number) ISBN:
0-9553018-0-7 20th
European Conference on Modelling and Simulation, Bonn,
May 28-31, 2006 |
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Citation
format: |
Lee, L. W – this is replaced by the citation format from
crossref for the specific paper |
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DOI: |
http://dx.doi.org/10.7148/2006-0266 |
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Abstract: |
Simulation of
the integrated urban wastewater system is a computationally-demanding task,
and performing long-term simulations consequently takes a considerable time.
Reduction of simulation times can be achieved by speeding up one
or more of the submodels of the integrated system. In this paper the use of a
fast neural network model instead of the mechanistic model of the wastewater
treatment plant (WWTP) is proposed for this purpose. The neural network is
trained on a sequence of treatment plant input/output data generated by the mechanistic
model of the WWTP, i.e. it is a reduced model of the original WWTP model. As
a result of model substitution a reduction of the simulation time by a factor
of 23 was achieved. The results presented in this paper show that the errors
introduced by the WWTP model substitution are of an acceptable level,
confirming the practical usefulness of the proposed method. |
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