Simulation study of model predictive control applied to binary distillation column

Abstract

Distillation columns are essential in many industries for separating liquids based on the volatility of their components. However, traditional control methods often struggle to achieve high component purity due to the process's complexity. This project investigates the use of Model Predictive Control (MPC) on a binary distillation column, comparing its performance to conventional control techniques. Guidelines for tuning the MPC controller are also provided. Simulation results show that MPC greatly improves setpoint tracking and disturbance rejection compared to traditional methods, demonstrating its potential to enhance process efficiency and product quality.