Reference : Supervised learning for a Kraft recovery boiler: a data mining approach with Random Fore...
Scientific congresses and symposiums : Unpublished conference
Engineering, computing & technology : Energy
http://hdl.handle.net/2268/90660
Supervised learning for a Kraft recovery boiler: a data mining approach with Random Forests.
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) >]
Lafourcade, Sébastien [PEPITe Technologies Inc. > > > >]
Jun-2010
Yes
No
International
ecos2010 - 23rd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems
du 14 juin au 17 juin 2010
EPFL - Ecole Polytechnique de Lausanne
Lausanne
Suisse
[en] data mining ; Random Forests ; Kraft recovery boiler ; steam production
[en] A data mining methodology, the random forests, is applied to predict high pressure steam
production from the recovery boiler of a Kraft pulping process. Starting from a large database of raw process data, the goal is to identify the input variables that explain the most significant output variations and to predict the high pressure steam flow.
http://hdl.handle.net/2268/90660

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Restricted access
Slides-Sainlez-Ecos.pdfAuthor preprint2.41 MBRequest copy

Bookmark and Share SFX Query

All documents in ORBi are protected by a user license.