State Space Modeling and Predictive Control of a Binary Batch Distillation Column

Abstract

A linear discrete state-space model of a methanol/water binary batch distillation column is developed based on theoretical analysis of dynamic mass balance and vapor-liquid phase balance, and this state-space model is used to design a model predictive control (MPC) strategy. The composition of methanol inside the distillation column is estimated using an empirical temperature-composition relationship model. The state space model based MPC algorithm is presented in detail, and the MPC strategy is implemented on an industrial control computer to directly control the estimated composition of the batch distillation column. Control experiments of the batch distillation column show that MPC gives smooth and accurate control results, and its control results are much better than the commonly used PI control