Reference : Supervised learning for a Kraft recovery boiler: a data mining approach with Random F...
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Energy
http://hdl.handle.net/2268/94231
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 > > > >]
1-Jan-2011
ECOS 2010 Volume IV (Power plants and Industrial processes)
Favrat, Daniel
Maréchal, François
235
Yes
No
International
145630318X
ECOS 2010 - 23rd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems
June 14, 2010 – June 17, 2010
EPFL
Lausanne
Suisse
[en] data mining ; Random Forests ; Kraft recovery boiler
[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/2268/94231

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