Reference : Supervised learning for a Kraft recovery boiler: a data mining approach with Random Fore...

	Document type :	Scientific congresses and symposiums : Unpublished conference
	Discipline(s) :	Engineering, computing & technology : Energy
	To cite this reference:	http://hdl.handle.net/2268/90660

Title :	Supervised learning for a Kraft recovery boiler: a data mining approach with Random Forests.
Language :	English
Author, co-author :	Sainlez, Matthieu [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. > > > >]
Publication date :	Jun-2010
Peer reviewed :	Yes
On invitation :	No
Audience :	International
Event name :	ecos2010 - 23rd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems
Event date :	du 14 juin au 17 juin 2010
Event organizer :	EPFL - Ecole Polytechnique de Lausanne
Event place (city) :	Lausanne
Event country :	Suisse
Keywords :	[en] data mining ; Random Forests ; Kraft recovery boiler ; steam production
Abstract :	[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.
Permalink :	http://hdl.handle.net/2268/90660

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	Fulltext file(s): File Commentary Version Size Access Restricted access Slides-Sainlez-Ecos.pdf Author preprint 2.41 MB Request copy

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