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Development of a Soft Sensor for a Thermal Cracking Unit using a small experimental data set

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5 Author(s)
Di Bella, A. ; Univ. degli Studi di Catania, Catania ; Fortuna, L. ; Graziani, S. ; Napoli, G.
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In this paper we compare a number of strategies to cope with the problem of small data sets in the identification of a nonlinear process. Four methods are analyzed: expansion of the training set by adding zero-mean fixed-variance Gaussian noise, expansion of the training set by adding zero-mean gaussian noise variance variable according with signal amplitude, integration between bootstrap method and stacked neural networks, and a new method based on the integration of bootstrap method, of the noise injection method, and of stacked neural networks. Such methods have been applied to develop a soft sensor for a thermal cracking unit working in a refinery in Sicily, Italy.

Published in:

Intelligent Signal Processing, 2007. WISP 2007. IEEE International Symposium on

Date of Conference:

3-5 Oct. 2007