Abstract
Worldwide, steel and aluminum production and manufacturing is still one of the major basic industries with a huge amount of material and energy consumption. Hence, optimization of the various process control schemes which are involved can lead to significant savings. Artificial Neural Networks are a new information processing technique which provides a novel approach to process control problems and promises major improvements. Therefore, Siemens together with FORWISS has been studying and developing neural control schemes for a number of different process control problems which occur at hot line rolling mills (Lindhoff et al., 1994). In this paper we give a brief survey of the different control aspects which were tackled with this new approach and comment on their current status.
FORWISS is the German acronym for “Bavarian Research Center for Knowledge-Based Systems”.
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References
Lindhoff, D., Sörgel, G., Gramckow, O., and Klode, K.-D. (1994). “Erfahrungen beim Einsatz Neuronaler Netze in der Walzwerksautomatisierung”. Stahl und Eisen, 114, Heft 4, S. 49–53+208.
Poppe, T. and Martinetz, T. (1993). “Estimating Material Properties for Process Optimization”. Proc. of the International Conference on Artificial Neural Networks–ICANN ‘83, Amsterdam, 13–16 Sep. 1993, Springer Verlag, pp. 795–798.
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© 1995 Springer-Verlag London Limited
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Martinetz, T., Protzel, P., Gramckow, O., Sörgel, G. (1995). Neural Network Control for Steel Rolling Mills. In: Kappen, B., Gielen, S. (eds) Neural Networks: Artificial Intelligence and Industrial Applications. Springer, London. https://doi.org/10.1007/978-1-4471-3087-1_55
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DOI: https://doi.org/10.1007/978-1-4471-3087-1_55
Publisher Name: Springer, London
Print ISBN: 978-3-540-19992-2
Online ISBN: 978-1-4471-3087-1
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