Simulation and Control of an Aromatic Distillation Column

Abstract

In general, the objective of distillation control is to maintain the desired products quality. In this paper, the performances of different one point control strategies for an aromatic distillation column have been compared through dynamic simulation. These methods are: a) Composition control using measured composition directly. This method sufferes from large sampling delay of measuring devices. b) Composition control by controlling the temperature of a specific tray. In this strategy, the composition-temperature relationship is used to find the temperature setpoint corresponding to the desired composition. Since composition-temperature relation depends on feed condition, an artificial neural network has been proposed which receives the feed specifications and provides the setpoint of the temperature control loop. c) Using temperature measurements for predicting the composition and controlling the composition based on predicted values of composition (inferential contol). Simulation results indicate that controlling the 8th tray temperature and using an artificial neural network for calculating corresponding tray temperature setpoint, has the best performance. Due to negligible pressure drop along the column, controlling the tray temperature difference does not improve the control loop performance.