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. © 2011 Springer-Verlag.
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Colla, V., Vannucci, M., Matarese, N., Stephens, G., Pianezzola, M., Alonso, I., … Schiewe, S. (2011). Detection of transients in steel casting through standard and AI-based techniques. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6691 LNCS, pp. 256–264). https://doi.org/10.1007/978-3-642-21501-8_32
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