Cross correlation analysis of residuals for the selection of the structure of virtual sensors in a refinery

In this paper the problem of regressor selection in virtual sensor design is addressed. In particular nonlinear models designed by experimental data are used to estimate relevant process variables of an industrial plant. The plant considered is a Sulphur Recovery Unit of a large refinery settled in Sicily. The proposed approach is used to face with the problem of input regressor selection of NMA models. The approach is based on a recursive evaluation of the cross correlation function between input variables and model residuals. The obtained results are compared with corresponding estimation obtained by using a reference model. Significant improvements in the model estimation capability show the suitability of the proposed method