Reference : Performance monitoring of an industrial boiler: classification of relevant variables ...
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Chemical engineering
http://hdl.handle.net/2268/90657
Performance monitoring of an industrial boiler: classification of relevant variables with Random Forests
English
Sainlez, Matthieu mailto [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) >]
2010
First edition 2010
20th European Symposium on Computer Aided Process Engineering – ESCAPE20
Pierucci, Sauro
Ferraris, Guido Buzzi
Elsevier
Computer-Aided Chemical Engineering, 28
403-408
Yes
No
International
978-0-444-53569-6
Amsterdam
The Netherlands
ESCAPE20 European Symposium on Computer Aided Engineering
du 6 juin au 9 juin 2010
AIDIC - The Italian Association of Chemical Engineering
Ischia
Italy
[en] data mining ; random forests ; Kraft pulping process ; recovery boiler ; atmospheric pollutants
[en] A data mining methodology, the random forests, is applied to analyze pollutant
emission 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 output variations.
http://hdl.handle.net/2268/90657

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