Variable selection and grouping in a paper machine application
Applications of Large Scale Industrial Systems, Volume # 1 | Part# 1
Timo Ahola; Esko Juuso; Kauko Leiviskä
Digital Object Identifier (DOI)
variable selection,grouping,paper machine,web breaks
This 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.
 Abrahamsson, C., J. Johansson, A. Sparén and F. Lindgren. Comparison of different variable selection methods conducted on NIR transmission measurements on intact tablets. Chemometrics and Intelligent Laboratory Systems, 69(2003)1- 2, 3-12.  Ahola, T. and K. Leiviskä. Case-based reasoning in web break sensitivity evaluation in a paper machine. Journal of Advanced Computational Intelligence and Intelligent Informatics. 9(2005)5.  Ahola, T. Intelligent estimation of web break sensitivity in paper machines. Acta Universitatis Ouluensis C 232. Oulu, 2005: University of Oulu.  Alexandridis, A., 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, 75(2005)2, 149- 162.  Arakawa, M., 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. Article in Press.  Cadima, J., J. Orestes Cerdeira and M. Minhoto. Computational aspects of algorithms for variable selection in the context of principal components. Computational Statistics & Data Analysis, 47(2004)2, 225-236.  Chiang L. H. and R. J. Pell. Genetic algorithms combined with discriminant analysis for key variable identification. Journal of Process Control, 14(2004)2, 143-155.  Cocchi, M., J. L. Hidalgo-Hidalgo-de-Cisneros, I. Naranjo-Rodríguez, J. M. Palacios-Santander, R. Seeber and A. Ulrici. Multicomponent analysis of electrochemical signals in the wavelet domain. Talanta, 59(2003)4, 735-749.  Dieterle, F., S. Busche and G. Gauglitz. Growing neural networks for a multivariate calibration and variable selection of time-resolved measurements. Analytica Chimica Acta, 490(2003)1- 2, 71-83.  Drezga, I., S. Rahman. Input variable selection for ANN-based short-term load forecasting. Power Systems, IEEE Transactions on, 13(1998)4, 1238-1244.  Gourvénec, S., X. Capron and D. L. Massart. Genetic algorithms (GA) applied to the orthogonal projection approach (OPA) for variable selection. Analytica Chimica Acta, 519(2004)1, 11-21.  Gributs C. E. W. and D. H. Burns. Parsimonious calibration models for near-infrared spectroscopy using wavelets and scaling functions. Chemometrics and Intelligent Laboratory Systems. Article in Press.  Gualdrón, O., 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, 114(2006)1, 522-529.  Isokangas A. and M. Ruusunen. Systematic approach for data survey. In the Proceedings of the International Conference on Informatics in Control, Automation and Robotics. September 14-17, 2005, Barcelona, Spain. pp. 60-65.  Juuso, E. K. Integration of intelligent systems in developing of smart adaptive systems, International Journal of Approximate Reasoning, 35(2004)307-337.  Lima, S. L. T., 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, 76(2005)1, 73-78.  Llobet, E., J. Brezmes, O. Gualdrón, X. Vilanova and X. Correig. Building parsimonious fuzzy ART-MAP models by variable selection with a cascaded genetic algorithm: application to multisensor systems for gas analysis. Sensors and Actuators B: Chemical, 99(2004)2-3, 267-272.  Narayanan R. and S. B. Gunturi. In silico ADME modelling: prediction models for blood-brain barrier permeation using a systematic variable selection method. Bioorganic & Medicinal Chemistry, 13(2005)8, 3017-3028.  Norinder, U. Support vector machine models in drug design: applications to drug transport processes and QSAR using simplex optimisations and variable selection. Neurocomputing, 55(2003)1- 2, 337-346.  Pontes, M. J. C., R. Kawakami H. Galvão, M. C. Ugulino Araújo, P. N. Teles Moreira, O. D. Pessoa Neto, G.E. José and T. C. Bezerra Saldanha. The successive projections algorithm for spectral variable selection in classification problems. Chemometrics and Intelligent Laboratory Systems, 78(2005)1-2, 11-18.  Shen, Q., 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, 22(2004)2-3, 145-152.  Smith, M., B. Pütz, D. Auer and L. Fahrmeir. Assessing brain activity through spatial bayesian variable selection. NeuroImage, 20(2003)2, 802- 815.  Stordrange, L., T. Rajalahti and F. O. Libnau. Multiway methods to explore and model NIR data from a batch process. Chemometrics and Intelligent Laboratory. Systems, 70(2004)2, 137-145.  Westad, F., M. Hersleth, P. Lea and H. Martens. Variable selection in PCA in sensory descriptive and consumer data. Food Quality and Preference, 14(2003)5-6, 463-472.  Zarzo M. and A Ferrer. Batch process diagnosis: PLS with variable selection versus block-wise PCR. Chemometrics and Intelligent Laboratory Systems, 73(2004)1, 15-27.