Abstract
The Gas Treatment Center performs a key role in the aluminum smelting process, since it strongly influences the chemical and thermal stability of the electrolytic bath through fluoridated alumina. Therefore this variable should be considered to keep the bath chemistry under control. However, the fluorine concentration measurement in fluoridated alumina is very time-consuming and that information becomes available only after a while. By using Artificial Neural Network we developed a Soft Sensor capable to estimate the fluorine concentration in fluoridated alumina, and to provide that information to plant engineers in a timely manner. This paper discusses the methodology used and the results of an implemented Soft Sensor using Neural Networks on fluorine estimation in fluoridated alumina from a Gas Treatment Center in an important Brazilian Aluminum Smelter.
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de Souza, A.M.F., de M. Affonso, C., Soares, F.M., de Oliveira, R.C.L. (2012). Soft Sensor for Fluoridated Alumina Inference in Gas Treatment Centers. In: Yin, H., Costa, J.A.F., Barreto, G. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2012. IDEAL 2012. Lecture Notes in Computer Science, vol 7435. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32639-4_36
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DOI: https://doi.org/10.1007/978-3-642-32639-4_36
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