Reference : Machine learning techniques for atmospheric pollutant monitoring
Scientific congresses and symposiums : Poster
Engineering, computing & technology : Multidisciplinary, general & others
http://hdl.handle.net/2268/116591
Machine learning techniques for atmospheric pollutant monitoring
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) >]
27-Jan-2012
A0
No
Yes
National
PhD day ENVITAM-GEPROC
27 janvier 2012
ENVITAM Graduate School and GEPROC Graduate School
Gembloux
Belgique
[en] Machine learning techniques are compared
to predict nitrogen oxide (NOx) pollutant
emission from the recovery boiler of a Kraft
pulp mill. Starting from a large database of
raw process data related to a Kraft recovery
boiler, we consider a regression problem in
which we are trying to predict the value of a
continuous variable. Generalization is done
on the worst case configuration possible to
make sure the model is adequate: the training
period concerns stationary operations while
test periods mainly focus on NOx emissions
during transient operations.
Researchers
http://hdl.handle.net/2268/116591

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