A new strategy to improve generalization capabiliy of data-driven soft sensor for industrial processes is proposed in the paper. The method is useful when a limited data set is available. The proposed approach is based on the integration of bootstrap resampling, noise injection and stacked neural networks. In the paper it has been applied to develop a Soft Sensor for the estimation of the Freezing Point of Kerosene in an atmospheric distillation unit (Topping) working in a refinery in Sicily, Italy. © 2009 IFAC.
Improving generalization in Neural Soft Sensor design
Caponetto R.;Graziani S.;
2009-01-01
Abstract
A new strategy to improve generalization capabiliy of data-driven soft sensor for industrial processes is proposed in the paper. The method is useful when a limited data set is available. The proposed approach is based on the integration of bootstrap resampling, noise injection and stacked neural networks. In the paper it has been applied to develop a Soft Sensor for the estimation of the Freezing Point of Kerosene in an atmospheric distillation unit (Topping) working in a refinery in Sicily, Italy. © 2009 IFAC.File in questo prodotto:
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