Identification of a Pilot Scale Distillation Column: A Kernel Based Approach

This paper describes the identification of a binary distillation column with Least-Squares Support Vector Machines (LS-SVM). It is our intention to investigate whether a kernel based model, particularly an LS-SVM, can be used for the simulation of the top and bottom temperature of a binary distillation column. Furthermore, we compare the latter model with standard linear models by means of mean-squared error (MSE). It will be demonstrated that this nonlinear model class achieves higher performances in MSE than linear models in the presence of nonlinear distortions. When the system is close to linear, the performance of the LS-SVM is only slightly better than the linear models.