Stacking approaches for the design of Soft Sensors using small data set

In this paper a number of approaches to design a soft sensor for an industrial plant in case of small data set are compared. In particular different strategies to aggregate suboptimal models obtained by bootstrapped neural networks and noise injection are considered. An industrial case of study, consisting in the estimation of the T95% of a Thermal Cracking Unit (TCU) of a refinery in Sicily is considered to evaluate the performance of the different approaches. © 2008 IEEE.