Nonlinear Modelling Application in Distillation Column

Abstract

Distillation columns are widely used in chemical processes and exhibit nonlinear dynamic behavior. In order to gain optimum performance of the distillation column, an effective control strategy is needed. In recent years, model based control strategies such as internal model control (IMC) and model predictive control (MPC) have been revealed as better control systems compared to the conventional method. But one of the major challenges in developing this effective control strategy is to construct a model which is utilized to describe the process under consideration. The purpose of this paper is to provide a review of the models that have been implemented in continuous distillation columns. These models are categorized under three major groups: fundamental models, which are derived from mass, energy and momentum balances of the process, empirical models, which are derived from input-output data of the process, and hybrid models which combine both the fundamental and the empirical model. The advantages and limitations of each group are discussed and compared. The review reveals a remarkable prospect of developing a nonlinear model in this research area. It also shows the discovery of new advance methods in an attempt to gain a nonlinear model that is able to be used in industries. Neural network models have become the most popular framework in nonlinear model development over the last decade even though hybrid models are the most promising method to be applied for future nonlinear model development.