Abstract
Flotation circuits play an important role in extracting valuable minerals from the ore. To control this process, the level is used to manipulate either the concentrate or the tailings grade. One of the key elements in controlling the level of a flotation cell is the control valve. The timely detection of any problem in these valves could mean big operational savings. This paper compares two Artificial Neural Network architectures for detecting clogging in control valves. The first one is based on the traditional autoassociative feedforward architecture with a bottleneck layer and the other one is based on discrete principal curves. We show that clogging can can be promptly detected by both methods; however, the second alternative can carry out the detection more efficiently than the first one.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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References
Cherkassky, V., Mulier, P.: Learning from data. John Wiley & Sons, New York (1997)
Bask, M., Johansson, A.: Model-based supervision of valves in flotation process. In: CDC 2003, Proceedings of the 42nd IEEE Conference on Decision and Control, December, pp. 744–749 (2003)
Stendlund, B., Medvedev, A.: Supervision of control valves in a series of cascade coupled flotation tanks. In: 4th IFAC Workshop on on-line falut detection and supervision in chemical process industries, December 2001, pp. 320–325 (2001)
Kramer, M.A.: Nonlinear principal Component analysis using autoassociative neural networks. AIChE J. 37(4), 233–243 (1991)
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© 2005 Springer-Verlag Berlin Heidelberg
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Sbarbaro, D., Carvajal, G. (2005). Supervision of Control Valves in Flotation Circuits Based on Artificial Neural Network. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_71
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DOI: https://doi.org/10.1007/11550907_71
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