Identification of a Noninsulated Distillation Column From Transient Response Data

Distillation is the most used separation technique worldwide. However, it is also an energy-intensive method. A way to reduce the energy consumption is to optimize the working conditions of distillation processes. This paper uses system identification to find an accurate model of a distillation column that can be used in, for example, model predictive control. Identification of a pilot-scale distillation column is done in the frequency domain in two steps. First, a nonparametric local polynomial method (LPM) is used to eliminate the leakage effects and to estimate the frequency response and noise contributions. A fast and a robust LPMs are compared. Second, results from the first step are used to find a low-order rational Laplace model with a time-delay term. Due to lack of insulation, the system's operating point is varying with ambient temperature. By using a reparametrization of the initial model, it is possible to account for these changes. The final result is a metamodel, which depends on the frequency and the effective separation heat of the column. In the future, this model can be applied in model predictive control to optimize the working conditions of a distillation column. Simulation data and real measurements on a pilot-scale distillation column are used to illustrate the results.