Reference : Kraft RB : recurrent neural network prediction of steam production
Scientific congresses and symposiums : Poster
Engineering, computing & technology : Energy
http://hdl.handle.net/2268/94245
Kraft RB : recurrent neural network prediction of steam production
English
Sainlez, Matthieu mailto [Université de Liège - ULg > > > Form.doct. sc. ingé. (chim. appl. - Bologne)]
Heyen, Georges [Université de Liège - ULg > Département de chimie appliquée > LASSC (Labo d'analyse et synthèse des systèmes chimiques) >]
30-May-2011
A0
Yes
No
International
ESCAPE21
May 29 - June 1, 2011
EFCE - European Federation of Chemical Engineering
Chalkidiki
Greece
[en] In this study, neural networks approaches are
compared for predicting the high pressure
(HP) steam flow rate from a Kraft recovery
boiler. We apply two types of neural networks:
a static multilayer perceptron and a dynamic
Elman’s recurrent neural network.
Starting from a one-day database of raw
process data related to the boiler, the goal
is to model and predict the next 12-hours of
HP steam flow production from the boiler to
the steam turbine. The results illustrate the
potential of the dynamic approach in this task.
http://hdl.handle.net/2268/94245

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