Variable Selection and Grouping in a Paper Machine Application
Keywords:variable selection, grouping, paper machine, web breaks
AbstractThis paper describes the possibilities of variable selection in large-scale industrial systems. It introduces knowledge-based, data-based and model-based methods for this purpose. As an example, Case-Based Reasoning application for the evaluation of the web break sensitivity in a paper machine is introduced. The application uses Linguistic Equations approach and basic Fuzzy Logic. The indicator combines the information of on-line measurements with expert knowledge and provides a continuous indication of the break sensitivity. The web break sensitivity defines the current operating situation at the paper mill and gives new information to the operators. Together with information of the most important variables this prediction gives operators enough time to react to the changing operating situation.
C. Abrahamsson, J. Johansson, A. SparÃ©n, and F. Lindgren, Comparison of different variable selection methods conducted on nir transmission measurements on intact tablets, Chemometrics and Intelligent Laboratory Systems, Vol. 69, pp. 3-12, 2003. http://dx.doi.org/10.1016/S0169-7439(03)00064-9
C.E.W. Gributs and D.H. Burns. Parsimonious calibration models for near-infrared spectroscopy using wavelets and scaling functions, Chemometrics and Intelligent Laboratory Systems, Vol. 83, pp. 44-53, 2006. http://dx.doi.org/10.1016/j.chemolab.2005.12.007
S. GourvÃ©nec, X. Capron, and D. L. Massart, Genetic algorithms (GA) applied to the orthogonal projection approach (OPA) for variable selection, Analytica Chimica Acta, Vol. 519, pp. 11-21, 2004. http://dx.doi.org/10.1016/j.aca.2004.05.023
L. Stordrange, T. Rajalahti, and F.O. Libnau, Multiway methods to explore and model NIR data from a batch process, Chemometrics and Intelligent Laboratory Systems, Vol. 70, pp. 137-145, 2004. http://dx.doi.org/10.1016/j.chemolab.2003.10.010
S.L.T. Lima, C. Mello, and R. J. Poppi, PLS pruning: a new approach to variable selection for multivariate calibration based on hessian matrix of errors, Chemometrics and Intelligent Laboratory Systems, Vol. 76, pp. 73-78, 2005. http://dx.doi.org/10.1016/j.chemolab.2004.09.007
M.J.C. Pontes, R. Kawakami, H. GalvÃ£o, M.C. Ugulino Araújo, P.N. Teles Moreira, O.D. Pessoa Neto, G.E. JosÃ©, and T.C. Bezerra Saldanha, The successive projections algorithm for spectral variable selection in classification problems, Chemometrics and Intelligent Laboratory Systems, Vol. 78, pp. 11-18, 2005. http://dx.doi.org/10.1016/j.chemolab.2004.12.001
M. Arakawa, K. Hasegawa, and K. Funatsu, QSAR study of anti-HIV HEPT analogues based on multi-objective genetic programming and counter-propagation neural network, Chemometrics and Intelligent Laboratory Systems, Vol. 83, pp. 91-98, 2006. http://dx.doi.org/10.1016/j.chemolab.2006.01.009
Q. Shen, J.-H. Jiang, C.-X. Jiao, G. Shen, and R.-Q. Yu, Modified particle swarm optimization algorithm for variable selection in MLR and PLS modeling: QSAR studies of antagonism of angiotensin II antagonists, European Journal of Pharmaceutical Sciences, Vol. 22, pp. 145-152, 2004. http://dx.doi.org/10.1016/j.ejps.2004.03.002
U. Norinder, Support vector machine models in drug design: applications to drug transport processes and qsar using simplex optimisations and variable selection, Neurocomputing, Vol. 55, pp. 337-346, 2003. http://dx.doi.org/10.1016/S0925-2312(03)00374-6
M. Smith, B. Pütz, D. Auer, and L. Fahrmeir, Assessing brain activity through spatial bayesian variable selection, NeuroImage, Vol. 20, pp. 802-815, 2003. http://dx.doi.org/10.1016/S1053-8119(03)00360-4
R. Narayanan and S.B. Gunturi, In silico ADME modelling: prediction models for bloodUË brain barrier permeation using a systematic variable selection method, Bioorganic & Medicinal Chemistry, Vol. 13, pp. 3017-3028, 2005. http://dx.doi.org/10.1016/j.bmc.2005.01.061
E. Llobet, J. Brezmes, O. GualdrÃ³n, X. Vilanova, and X. Correig, Building parsimonious fuzzy ARTMAP models by variable selection with a cascaded genetic algorithm: application to multisensor systems for gas analysis, Sensors and Actuators B: Chemical, Vol. 99, pp. 267-272, 2004. http://dx.doi.org/10.1016/j.snb.2003.11.019
O. GualdrÃ³n, E. Llobet, J. Brezmes, X. Vilanova, and X. Correig, Coupling fast variable selection methods to neural network-based classifiers: Application to multisensor systems, Sensors and Actuators B: Chemical, Vol. 114, pp. 522-529, 2006. http://dx.doi.org/10.1016/j.snb.2005.04.046
M. Cocchi, J.L. Hidalgo-Hidalgo de Cisneros, I. Naranjo-RodrÃguez, J.M. Palacios-Santander, R. Seeber, and A. Ulrici, Multicomponent analysis of electrochemical signals in the wavelet domain, Talanta, Vol. 59, pp. 735-749, 2003. http://dx.doi.org/10.1016/S0039-9140(02)00615-X
F.Westad, M. Hersleth, P. Lea, and H. Martens, Variable selection in pca in sensory descriptive and consumer data, Food Quality and Preference, Vol. 14, pp. 463-472, 2003. http://dx.doi.org/10.1016/S0950-3293(03)00015-6
J. Cadima, J. Orestes Cerdeira, and M. Minhoto, Computational aspects of algorithms for variable selection in the context of principal components. Computational Statistics & Data Analysis, 47(2):225-236, 2004. http://dx.doi.org/10.1016/j.csda.2003.11.001
M. Zarzo and A Ferrer, Batch process diagnosis: PLS with variable selection versus block-wise PCR, Chemometrics and Intelligent Laboratory Systems, Vol. 73, pp. 15-27, 2004. http://dx.doi.org/10.1016/j.chemolab.2003.11.009
L. H. Chiang and R.J. Pell, Genetic algorithms combined with discriminant analysis for key variable identification, Journal of Process Control, Vol. 14, pp. 143-155, 2004. http://dx.doi.org/10.1016/S0959-1524(03)00029-5
A. Alexandridis, P. Patrinos, H. Sarimveis, and G. Tsekouras, A two-stage evolutionary algorithm for variable selection in the development of RBF neural network models, Chemometrics and Intelligent Laboratory Systems, Vol. 75, pp. 149-162, 2005. http://dx.doi.org/10.1016/j.chemolab.2004.06.004
F. Dieterle, S. Busche, and G. Gauglitz, Growing neural networks for a multivariate calibration and variable selection of time-resolved measurements, Analytica Chimica Acta, Vol. 490, pp. 71-83, 2003. http://dx.doi.org/10.1016/S0003-2670(03)00338-6
I. Drezga and S. Rahman, Input variable selection for ann-based short-term load forecasting, Power Systems, IEEE Transactions on, Vol. 13, pp. 1238-1244, 1998.
E. K. Juuso, Integration of intelligent systems in development of smart adaptive systems, International Journal of Approximate Reasoning, Vol. 35, pp. 307-337, 2004. http://dx.doi.org/10.1016/j.ijar.2003.08.008
A. Isokangas and M. Ruusunen, Systematic approach for data survey, in Proceedings of the International Conference on Informatics in Control, Automation and Robotics. September 14 - 17, 2005, Barcelona, Spain, pp. 60-65, 2005.
T. Ahola, Intelligent estimation of web break sensitivity in paper machines. Doctoral dissertation. University of Oulu, Department of Process and Environmental Engineering. Acta Universitatis Ouluensis, Technica C 232, 92 p., Oulu, 2005.
T. Ahola and K. LeiviskÃ¤, Case-based reasoning in web break sensitivity evaluation in a paper machine, Journal of Advanced Computational Intelligence and Intelligence Informatics, Vol. 9, pp. 555-561, 2005.
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.