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Sainlez Matthieu

Main Referenced Co-authors
Heyen, Georges  (11)
Lafourcade, Sébastien (2)
Absil, Pierre-Antoine (1)
Lumen, Philippe (1)
Teschendorff, Andrew E. (1)
Main Referenced Keywords
data mining (3); Kraft recovery boiler (3); Random Forests (2); steam production (2); atmospheric pollutants (1);
Main Referenced Disciplines
Chemical engineering (6)
Engineering, computing & technology: Multidisciplinary, general & others (6)
Computer science (4)
Energy (3)
Mathematics (2)

Publications (total 21)

The most downloaded
7 downloads
Sainlez, M., & Heyen, G. (2012). Comparison of supervised learning techniques for atmospheric pollutant monitoring in a Kraft pulp mill. Journal of Computational and Applied Mathematics. doi:10.1016/j.cam.2012.06.026 https://hdl.handle.net/2268/124077

The most cited

10 citations (Scopus®)

Sainlez, M., & Heyen, G. (2012). Comparison of supervised learning techniques for atmospheric pollutant monitoring in a Kraft pulp mill. Journal of Computational and Applied Mathematics. doi:10.1016/j.cam.2012.06.026 https://hdl.handle.net/2268/124077

Sainlez, M. (2012). Apprentissages automatiques supervisés pour le monitoring environnemental et énergétique d'une chaudière de régénération [Doctoral thesis, ULiège - Université de Liège]. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/133097

Sainlez, M., & Heyen, G. (27 January 2012). Machine learning techniques for atmospheric pollutant monitoring [Poster presentation]. PhD day ENVITAM-GEPROC, Gembloux, Belgium.

Sainlez, M., & Heyen, G. (2012). Comparison of supervised learning techniques for atmospheric pollutant monitoring in a Kraft pulp mill. Journal of Computational and Applied Mathematics. doi:10.1016/j.cam.2012.06.026
Peer Reviewed verified by ORBi

Sainlez, M. (15 November 2011). Comparison of Machine Learning techniques for atmospheric pollutant monitoring: a Kraft pulp mill case study [Paper presentation]. Fifth International Conference on Advanced COmputational Methods in ENgineering (ACOMEN 2011), Liège, Belgium.

Sainlez, M. (10 November 2011). Les réseaux de neurones artificiels dans la finance [Paper presentation]. Semaine Intégration pédagogique - HERS Libramont, Libramont, Belgium.

Sainlez, M., & Heyen, G. (November 2011). Comparison of Machine Learning techniques for atmospheric pollutant monitoring in a Kraft pulp mill [Paper presentation]. ACOMEN 2011, Liège, Belgium.

Sainlez, M., & Heyen, G. (30 May 2011). Kraft RB : recurrent neural network prediction of steam production [Poster presentation]. ESCAPE21, Chalkidiki, Greece.

Sainlez, M., Heyen, G., & Lumen, P. (27 May 2011). Approche neuronale dynamique pour la prédiction de polluants atmosphériques: application à l'industrie papetière [Paper presentation]. 43èmes Journées de Statistiques SFDS, Gammarth- Tunis, Tunisia.

Sainlez, M. (27 May 2011). Dynamic neural network approach for atmospheric pollutant prediction: A pulp mill case study [Paper presentation]. 43èmes Journées de Statistique de la SFdS, Gammarth - Tunis, Tunisia.

Sainlez, M. (30 March 2011). L'intelligence des données au service des industries [Paper presentation]. Conférence dans le cadre du Printemps des Sciences 2011, Arlon, Belgium.

Sainlez, M., & Heyen, G. (2011). Recurrent neural network prediction of steam production in a Kraft recovery boiler. In E. N. Pistikopoulos, M. C. Georgiadis, ... A. C. Kokossis (Eds.), 21st European Symposium on Computer Aided Process Engineering (Part B) (First edition 2011, pp. 1784-1788). Amsterdam, Netherlands: Elsevier.
Peer reviewed

Sainlez, M., Heyen, G., & Lafourcade, S. (2011). Supervised learning for a Kraft recovery boiler: a data mining approach with Random Forests. In D. Favrat & F. Maréchal (Eds.), ECOS 2010 Volume IV (Power plants and Industrial processes) (pp. 235).
Peer reviewed

Sainlez, M. (July 2010). Kraft recovery boiler analysis : a data mining approach [Poster presentation]. Summer School on Neural Networks in Classification, Regression and Data Mining, Porto, Portugal.

Sainlez, M., Heyen, G., & Lafourcade, S. (June 2010). Supervised learning for a Kraft recovery boiler: a data mining approach with Random Forests [Paper presentation]. ecos2010 - 23rd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, Lausanne, Switzerland.

Sainlez, M. (June 2010). Kraft RB : classification of relevant variables with Random Forests [Poster presentation]. ESCAPE20 European Symposium on Computer Aided Engineering, Ischia, Italy.

Sainlez, M., & Heyen, G. (2010). Performance monitoring of an industrial boiler: classification of relevant variables with Random Forests. In S. Pierucci & G. B. Ferraris (Eds.), 20th European Symposium on Computer Aided Process Engineering – ESCAPE20 (First edition 2010, pp. 403-408). Amsterdam, Netherlands: Elsevier.
Peer reviewed

Sainlez, M., & Heyen, G. (2010). Recovery boiler monitoring : steam flow prediction with random forests [Poster presentation]. CAPE Forum 2010, Aachen, Germany.

Sainlez, M. (April 2009). Gene expression data analysis using spatiotemporal blind source separation [Poster presentation]. ESANN2009 17th European Symposium on Artificial Neural Networks, Bruges, Belgium.

Sainlez, M., Absil, P.-A., & Teschendorff, A. E. (2009). Gene expression data analysis using spatiotemporal blind source separation. In M. Verleysen (Ed.), ESANN'2009 proceedings, European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning (pp. 159-163). Evere, Belgium: d-side.
Peer reviewed

Sainlez, M. (2009). Techniques avancées de data mining pour l’optimisation énergétique [Poster presentation]. Journée scientifique Inter Hautes Ecoles, Namur, Belgium.

Sainlez, M., & Heyen, G. (2008). Suivi de performances et optimisation de procédés industriels [Poster presentation]. Le Génie des Procédés au service du Développement Durable, Mons, Belgium.

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