Development of a Soft Sensor for a Thermal Cracking Unit using a small experimental data set

In this paper we compare a number of strategies to cope with the problem of small data sets in the identification of a nonlinear process. Four methods are analyzed: expansion of the training set by adding zero-mean fixed-variance Gaussian noise, expansion of the training set by adding zero-mean gaussian noise variance variable according with signal amplitude, integration between bootstrap method and stacked neural networks, and a new method based on the integration of bootstrap method, of the noise injection method, and of stacked neural networks. Such methods have been applied to develop a soft sensor for a thermal cracking unit working in a refinery in Sicily, Italy.