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Visual decision support system for the assessment of steel production efficiency

Published online by Cambridge University Press:  24 June 2010

S.R. Cuesta ,
A. García ,
V. Torre  and
S. Peregrina
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Abstract

A novel web-based Decision Support System (DSS) for condition monitoring and continuous improvement of steelmaking production efficiency is presented in this paper. Production efficiency was measured and modelled through Key Performance Indicators (KPIs), which are assigned on-line to a single product (coil) and identified at different facilities throughout the integrated route. The systemic nature of the DSS system boosts the integration of every agent included in the decision chain of the company. It assures multi-agent decision convergence and the transformation of process information and expert knowledge into profitable results. The first case study is presented.

Type
Research Article
Information
Metallurgical Research & Technology , Volume 107 , Issue 4 , April 2010 , pp. 145 - 154
Copyright
© EDP Sciences, 2010

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References

Draft reference document on best available techniques for the Production of Iron and Steel, February 2008, European IPCC Bureau
World steel in gures 2008, International Iron and Steel Institute, ISSN 1379-9746 2008
R.J. Fruehan et al., The future steelmaking industry and its technologies, Sloan steel industry competitiveness study, Carnegie Mellon University, Pennsylvania
European steel technology platform (ESTEP): From a strategic research agenda to implementation, March 2006
Steel industry. Marginal Opportunity Study, Energetics Inc. for the US Department of Energy, September 2005
Jiao, , Jianxin, and Tseng, M., Mitchell, , A pragmatic approach to product costing based on standard time estimation, International Journal of Operations & Production Management, 19 (1999) 738-755 CrossRefGoogle Scholar
T. Powell, The knowledge value Chain. How to fix it when it breaks, Proceedings of the 22nd National Online Meeting, Medford, NJ: Information Today, Inc
Fayyad, U., Piatetsky-Shapiro, G., From Data Mining to Knowledge Discovery in Databases, A Survey of Decision Support Systems Appl., AI Magazine, 17 (1996) 37-54 Google Scholar
P. Chapman, J. Clinton, R. Kerber, T. Khabaza, T. Reinartz, C. Shearer, R. Wirth, CRISP-DM 1.0, Step-by-step data mining guide
R.H. Sprague Jr., A framework for the development of decision support systems, Englewood Cliffs, NJ: Prentice
www.visualcomplexity.com
P. Zhang et al., Business information visualization for decision making support – a research strategy, First Americas conference on information systems, 1995 – USA
Information visualization and visual data mining, IEEE Transactions on Visualization and Computer Graphics, 8 (2002) 1-8 CrossRef
T. Kohonen, The Self-Organizing Map., Proceedings of the IEEE, 78 (1990) 1464-1480
L. Asker Zadeh, Outline of a New Approach to the Analysis of Complex Systems and Decision Processes, edited by R.R. Yager, S. Ovchinnikov, R.M. Tong, H.T. Nguyen, Fuzzy Sets and Applications: selected papers by L.A. Zadeh, John Wiley & Sons, Inc., New York, 1987, pp. 105-146
K. Hirota, W. Pedrycz., Fuzzy Computing for Data Mining, Proceedings of the IEEE, 87 (1999) 1575-1600
S. Mitra et al., Data mining in soft computing framework: a survey, IEEE Transactions on neural networks, 13 January 2002
I. Díaz, A.B. Diez, A.A. Cuadrado, M. Domìnguez, Prior knowledge integration in self organizing maps for complex process supervision, International Federation of Automatic Control 15th IFAC World Congress, 2002
S.R. Cuesta, I. Díaz, A.A. Cuadrado, A.B. Diez, A visual approach for fuzzy rule induction, International Conference on Emerging Technologies and Factory Automation ETFA’ 03
Friendly, M., Corrgrams, Exploratory displays for correlation matrices, American Statistician, 56 (2002) 316-324 CrossRefGoogle Scholar
J.R. Quinlan, Induction of Decision Trees, edited by Jude W. Shavlik, Thomas G. Dietterich, Readings in Machine Learning, M. Kaufmann, 1990, originally published in M. Learning, 1 (1986) 81-106
E. Keogh, S. Kasetty, On the need for time series data mining benchmarks, A survey and empirical demonstration, 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2002) 102-111
D.J. Power, Building Web-based Decision Support Systems, Studies on Informatics and Control, 11 (2002)