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

Sainlez, Matthieu; Heyen, Georges; Lafourcade, Sébastien

Unpublished conference/Abstract (Scientific congresses and symposiums)

Sainlez, Matthieu; Heyen, Georges; Lafourcade, Sébastien

2010 • ecos2010 - 23rd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems

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Keywords :

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.

Disciplines :

Energy

Author, co-author :

Sainlez, Matthieu ; 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.

Language :

English

Title :

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

Publication date :

June 2010

Event name :

ecos2010 - 23rd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems

Event organizer :

EPFL - Ecole Polytechnique de Lausanne

Event place :

Lausanne, Switzerland

Event date :

du 14 juin au 17 juin 2010

Audience :

International

Available on ORBi :

since 10 May 2011

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