The paper proposes a comparison of different strategies of regressors selection for the design of a Soft Sensor for a Sulfur Recovery Unit of a refinery. The Soft Sensor is designed to replace the on line analyzer during maintenance and it is designed by using nonlinear MA models implemented by a MLP neural network. A number of strategies for the automatic choice of influent input variables and regressors selection, on the basis of available experimental data, are compared with a strategy based on a trial and error approach, guided by the knowledge of the experts, both in terms of their performance and their computational complexity.
Comparing Regressors Selection Methods for the Soft Sensor Design of a Sulfur Recovery Unit
Fortuna, Luigi;Graziani, S;
2006-01-01
Abstract
The paper proposes a comparison of different strategies of regressors selection for the design of a Soft Sensor for a Sulfur Recovery Unit of a refinery. The Soft Sensor is designed to replace the on line analyzer during maintenance and it is designed by using nonlinear MA models implemented by a MLP neural network. A number of strategies for the automatic choice of influent input variables and regressors selection, on the basis of available experimental data, are compared with a strategy based on a trial and error approach, guided by the knowledge of the experts, both in terms of their performance and their computational complexity.File in questo prodotto:
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