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

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


	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Multidisciplinary, general & others
	To cite this reference:	http://hdl.handle.net/10993/16304

Title :	Supervised learning for a Kraft recovery boiler: a data mining approach with Random Forests.
Language :	English
Author, co-author :	Sainlez, Matthieu [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
	Heyen, Georges []
	Lafourcade, Sébastien []
Publication date :	11-Jan-2011
Main document title :	ECOS 2010 Volume IV (Power plants and Industrial processes)
Editor :	Favrat, Daniel
	Maréchal, François
Peer reviewed :	Yes
On invitation :	No
Audience :	International
ISBN :	145630318X
Event name :	ECOS 2010 - 23rd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems
Event date :	June 14, 2010 – June 17, 2010
Event organizer :	EPFL
Event place (city) :	Lausanne
Event country :	Suisse
Keywords :	[en] data mining ; Random Forests ; Kraft recovery boiler
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/10993/16304

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