Soft sensor for a Propylene Splitter with seasonal variations

The paper deals with the design of a data driven soft sensor, able to estimate propylene percentage in the bottom flow of a Propylene Splitter showing seasonal variations. Experimental data have been collected in a refinery in Sicily. The soft sensor is intended to replace the online analyzer during maintenance, in order to guarantee the desired plant performance. In order to take into account seasonal variations, two models have been designed and implemented by using MLP neural networks. Seasonal variations are mainly related to the temperature of seawater used in the plant for cooling that shows significant variations along the year. A set of fuzzy rules has been designed in order to allow a soft transition between the winter and the summer models. A comparison is performed with a neural model working on the whole data set, i.e. covering both winter and summer collected data.