Detection of transients in steel casting through standard and AI-based techniques

Valentina Colla; Marco Vannucci; Nicola Matarese; Gerard Stephens; Marco Pianezzola; Izaskun Alonso; Torsten Lamp; Juan Palacios; Siegfried Schiewe

doi:10.1007/978-3-642-21501-8_32

Detection of transients in steel casting through standard and AI-based techniques

Valentina Colla, Marco Vannucci, Nicola Matarese, Gerard Stephens, Marco Pianezzola, Izaskun Alonso, Torsten Lamp, Juan Palacios, Siegfried Schiewe

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

The detection of transients in the practice of continuous casting within a steel-making industry is a key task for the prediction of final product properties but currently a direct observation of this phenomenon is not available. For this reason in this paper several standard and soft-computing based methods for the detection of transients from plant data will be tested and compared. From the obtained results it emerges that the use of a fuzzy inference system based on experts knowledge achieves very satisfactory results correctly identifying most of the transient events present in the databases provided by different companies.

Original language	English
Title of host publication	Advances in Computational Intelligence - 11th International Work-Conference on Artificial Neural Networks, IWANN 2011, Proceedings
Pages	256-264
Number of pages	9
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-21501-8_32
Publication status	Published - 2011
Event	11th International Work-Conference on on Artificial Neural Networks, IWANN 2011 - Torremolinos-Malaga, Spain Duration: 8 Jun 2011 → 10 Jun 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	6691 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Work-Conference on on Artificial Neural Networks, IWANN 2011
Country/Territory	Spain
City	Torremolinos-Malaga
Period	8/06/11 → 10/06/11

Keywords

industrial problem
neuro-fuzzy systems
transient detection

Access to Document

10.1007/978-3-642-21501-8_32

Cite this

Colla, V., Vannucci, M., Matarese, N., Stephens, G., Pianezzola, M., Alonso, I., Lamp, T., Palacios, J., & Schiewe, S. (2011). Detection of transients in steel casting through standard and AI-based techniques. In Advances in Computational Intelligence - 11th International Work-Conference on Artificial Neural Networks, IWANN 2011, Proceedings (PART 1 ed., pp. 256-264). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6691 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-21501-8_32

Colla, Valentina ; Vannucci, Marco ; Matarese, Nicola et al. / Detection of transients in steel casting through standard and AI-based techniques. Advances in Computational Intelligence - 11th International Work-Conference on Artificial Neural Networks, IWANN 2011, Proceedings. PART 1. ed. 2011. pp. 256-264 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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title = "Detection of transients in steel casting through standard and AI-based techniques",

abstract = "The detection of transients in the practice of continuous casting within a steel-making industry is a key task for the prediction of final product properties but currently a direct observation of this phenomenon is not available. For this reason in this paper several standard and soft-computing based methods for the detection of transients from plant data will be tested and compared. From the obtained results it emerges that the use of a fuzzy inference system based on experts knowledge achieves very satisfactory results correctly identifying most of the transient events present in the databases provided by different companies.",

keywords = "industrial problem, neuro-fuzzy systems, transient detection",

author = "Valentina Colla and Marco Vannucci and Nicola Matarese and Gerard Stephens and Marco Pianezzola and Izaskun Alonso and Torsten Lamp and Juan Palacios and Siegfried Schiewe",

year = "2011",

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Colla, V, Vannucci, M, Matarese, N, Stephens, G, Pianezzola, M, Alonso, I, Lamp, T, Palacios, J & Schiewe, S 2011, Detection of transients in steel casting through standard and AI-based techniques. in Advances in Computational Intelligence - 11th International Work-Conference on Artificial Neural Networks, IWANN 2011, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 6691 LNCS, pp. 256-264, 11th International Work-Conference on on Artificial Neural Networks, IWANN 2011, Torremolinos-Malaga, Spain, 8/06/11. https://doi.org/10.1007/978-3-642-21501-8_32

Detection of transients in steel casting through standard and AI-based techniques. / Colla, Valentina; Vannucci, Marco; Matarese, Nicola et al.
Advances in Computational Intelligence - 11th International Work-Conference on Artificial Neural Networks, IWANN 2011, Proceedings. PART 1. ed. 2011. p. 256-264 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6691 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Colla, Valentina

AU - Vannucci, Marco

AU - Matarese, Nicola

AU - Stephens, Gerard

AU - Pianezzola, Marco

AU - Alonso, Izaskun

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AU - Palacios, Juan

AU - Schiewe, Siegfried

PY - 2011

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AB - The detection of transients in the practice of continuous casting within a steel-making industry is a key task for the prediction of final product properties but currently a direct observation of this phenomenon is not available. For this reason in this paper several standard and soft-computing based methods for the detection of transients from plant data will be tested and compared. From the obtained results it emerges that the use of a fuzzy inference system based on experts knowledge achieves very satisfactory results correctly identifying most of the transient events present in the databases provided by different companies.

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KW - neuro-fuzzy systems

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Colla V, Vannucci M, Matarese N, Stephens G, Pianezzola M, Alonso I et al. Detection of transients in steel casting through standard and AI-based techniques. In Advances in Computational Intelligence - 11th International Work-Conference on Artificial Neural Networks, IWANN 2011, Proceedings. PART 1 ed. 2011. p. 256-264. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-21501-8_32

Detection of transients in steel casting through standard and AI-based techniques

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