data mining; Random Forests; Kraft recovery boiler
Résumé :
[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.
Disciplines :
Ingénierie, informatique & technologie: Multidisciplinaire, généralités & autres
Auteur, co-auteur :
SAINLEZ, Matthieu ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Heyen, Georges
Lafourcade, Sébastien
Langue du document :
Anglais
Titre :
Supervised learning for a Kraft recovery boiler: a data mining approach with Random Forests.
Date de publication/diffusion :
11 janvier 2011
Nom de la manifestation :
ECOS 2010 - 23rd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems
Organisateur de la manifestation :
EPFL
Lieu de la manifestation :
Lausanne, Suisse
Date de la manifestation :
June 14, 2010 – June 17, 2010
Manifestation à portée :
International
Titre de l'ouvrage principal :
ECOS 2010 Volume IV (Power plants and Industrial processes)
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