Realtime Model Validation and Design of RTD-A Controller for UOP3CC Binary Distillation Column

Distillation columns are essential in the chemical and process industries for separating liquid mixtures based on differences in the volatility of their components. Achieving the desired product compositions requires effective modelling and control strategies. This work focuses on developing a dynamic mathematical model for the UOP3CC Armfield binary distillation column used in the laboratory. The model incorporates mass and energy balance equations and considers key factors such as heat transfer in the condenser and reboiler, tray hydraulics, and vapor-liquid equilibrium. The model parameters are estimated and validated using real-time experimental data. The control strategy aims to regulate the temperature profiles to maintain the desired ethanol composition in the top product. This work explores the implementation of Proportional-Integral (PI), Model Predictive Control (MPC), and Robust setpoint Tracking Disturbance rejection with Aggressiveness (RTD-A) controllers to optimize process performance. Model order reduction techniques simplify the complex process dynamics, while a decoupler is used to manage interactions between control loops. The performance of the PI, MPC, and RTD-A controllers is compared using metrics like setpoint tracking, disturbance rejection, and computational efficiency. Experiments are conducted to establish the relationship between top tray temperature and distillate purity, leading to the identification of a temperature setpoint that maximizes ethanol purity. This setpoint is validated in a closed-loop system, with the physical model achieving a steady-state error of just 0.19%. The results emphasize the significance of accurate modelling in improving the performance of the UOP3CC distillation column.