Reference : Machine learning techniques for atmospheric pollutant monitoring

	Document type :	Scientific congresses and symposiums : Poster
	Discipline(s) :	Engineering, computing & technology : Multidisciplinary, general & others
	To cite this reference:	http://hdl.handle.net/2268/116591

Title :	Machine learning techniques for atmospheric pollutant monitoring
Language :	English
Author, co-author :	Sainlez, Matthieu [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) >]
Publication date :	27-Jan-2012
Page number :	A0
Peer reviewed :	No
On invitation :	Yes
Audience :	National
Event name :	PhD day ENVITAM-GEPROC
Event date :	27 janvier 2012
Event organizer :	ENVITAM Graduate School and GEPROC Graduate School
Event place (city) :	Gembloux
Event country :	Belgique
Abstract :	[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.
Target :	Researchers
Permalink :	http://hdl.handle.net/2268/116591

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