Modeling of a distillation column based on NARMAX and Hammerstein models

Abstract

The modeling of distillation column process is a very challenging problem because of the complex dynamic behavior. This paper investigates a Nonlinear Autoregressive Moving Average with eXogenous input (NARMAX) model, and a Hammerstein model to approximate the evolution of the overhead temperature in a separation system. The model development and validation are studied through experiments carried out on a distillation plant of laboratory scale. Three model order selection criteria such as Aikeke’s Information Criterion (AIC), Root Mean Square Error (RMSE) and Nash–Sutcliffe Efficiency (NSE) are used to evaluate the prediction performance of the process behavior. The results illustrate that both models produce acceptable predictions but the NARMAX model outperforms the Hammerstein model.