Poster (Scientific congresses and symposiums)
Kraft RB : recurrent neural network prediction of steam production
Sainlez, Matthieu; Heyen, Georges
2011ESCAPE21
 

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Abstract :
[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.
Disciplines :
Energy
Author, co-author :
Sainlez, Matthieu ;  Université de Liège - ULiège > Form.doct. sc. ingé. (chim. appl. - Bologne)
Heyen, Georges ;  Université de Liège - ULiège > Département de chimie appliquée > LASSC (Labo d'analyse et synthèse des systèmes chimiques)
Language :
English
Title :
Kraft RB : recurrent neural network prediction of steam production
Publication date :
30 May 2011
Number of pages :
A0
Event name :
ESCAPE21
Event organizer :
EFCE - European Federation of Chemical Engineering
Event place :
Chalkidiki, Greece
Event date :
May 29 - June 1, 2011
Audience :
International
Available on ORBi :
since 27 June 2011

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