Reference : Supervised learning for a Kraft recovery boiler: a data mining approach with Random F...
Scientific congresses and symposiums : Unpublished conference/Abstract
Engineering, computing & technology : Energy
Supervised learning for a Kraft recovery boiler: a data mining approach with Random Forests.
Sainlez, Matthieu mailto [Université de Liège - ULiège > > > Form.doct. sc. ingé. (chim. appl. - Bologne)]
Heyen, Georges [Université de Liège - ULiège > Département de chimie appliquée > LASSC (Labo d'analyse et synthèse des systèmes chimiques) >]
Lafourcade, Sébastien [PEPITe Technologies Inc. > > > >]
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
[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.

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