Comparing regressors selection methods for the Soft Sensor design of a Sulfur Recovery Unit

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