data validation and reconciliation; data mining; soft sensors; process monitoring; process control
Abstract :
[en] Process monitoring is made difficult when measurements are subjected to errors, since pertinent information is hidden in the measurement noise. To address this issue, one can use model based data validation, or rely on statistical techniques to analyze large historical data sets (data mining). An industrial case study is presented here, where a model based approach (data validation) is compared to data driven techniques.
Disciplines :
Chemical engineering Chemistry
Author, co-author :
Duchesne, Arnaud
Heyen, Georges ; Université de Liège - ULiège > Département de chimie appliquée > LASSC (Labo d'analyse et synthèse des systèmes chimiques)
Mack, Philippe
Kalitventzeff, Boris ; Université de Liège - ULiège > Faculté des sciences appliquées > Professeur émérite
Language :
English
Title :
Process monitoring using a combination of data driven techniques and model based data validation
ARORA,N., BIEGLER,L.T., HEYEN, G.,Data Reconciliation Framework, in B. Braunschweig and R. GANI (eds) Software Architectures and Tools for Computer Aided Process Engineering, Elsevier, 2002
HEYEN, G., KALITVENTZEFF, B., Process monitoring and Data Reconciliation, in L. Puigjaner and G. Heyen (eds), Computer Aided Process Engineering, Wiley-VCH, 2006
KALITVENTZEFF, B., HEYEN, G., MATEUS TAVARES, M.,Data Validation, a Technology for intelligent Manufacturing, in L. Puigjaner and G. Heyen (eds), Computer Aided Process Engineering,.Wiley-VCH, 2006
http://www.belsim.com/Vali.aspx, accessed April 18,2007
http://www.pepite.be/en/produits/PEPITo, accessed April 18,2007
*** PEPITo Data Mining software UserGuide v1.5 (c) PEPITe SA, 2006
MACQUEEN, J. B., Some Methods for classification and Analysis of Multivariate Observations, Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of California Press, 1:281-297-1967
WEHENKEL LOUIS, Decision tree pruning using an additive information quality measure, Uncertainty in Intelligent Systems, Elsevier-North Holland, 1993, p. 397
BISHOP, C. M., Neural Network for Pattern Recognition. Clarendon Press, Oxford, 1995
FUKUNAGA, KEINOSUKE, Introduction to Statistical Pattern Recognition, Elsevier, 1990
