Control configurations in distillation columns: A comparative study

Model-plant mismatch in model predictive controlled industrial processes is a phenomenon that may lead to relevant performance losses. Research in finding a suitable procedure to effectively detect and revert model plant mismatch often uses distillation columns as benchmark testing models. However, it was found that sub optimal distillation columns and control configurations were used in these studies. This paper aims to provide answers for the issues as just discussed. At first a benchmark distillation column is designed using the work of Sigurd Skogestad. Secondly, two common control configurations are compared, in which it is shown that in high purity regions the a more accurate model can be identified in the (L/D)(V/B)-configuration. Further, in the case of disturbances on the feed rate and feed composition channels of the distillation column, it is shown that the (L/D)(V/B)-configuration out performs the LV-configuration in terms of disturbance rejection and handling model-plant mismatch.

I. Introduction

Model predictive control (MPC) is a control technique frequently used in the process industry. The two main advantages of model predictive controllers is that they are able to predict the impact of disturbances, and can accommodate for constraints on inputs and outputs of the dynamic systems they control. The design of model predictive controllers and their implementation in example industrial processes, most notably distillation columns, have been investigated thoroughly in literature, see for example [1] and [2]. In [3] and [4], non-linear MPC methods are investigated using a distillation column as benchmark.