Nonlinear model predictive control of a reactive distillation column

Abstract

Model Predictive Control (MPC) is an optimal-control based method to select control inputs by minimizing the predicted error from setpoint for the future. Industrially popular MPC algorithms use linear convolution models for predicting controlled variable response in future. For highly nonlinear processes, the linear MPC might not provide satisfactory performance. Nonlinear Model Predictive Control (NLMPC) employs nonlinear models of the process in the control algorithm for controlled variable response in future.

Reactive distillation modeling and control poses a challenging problem because the simultaneous separation and reaction leads to complex interactions between vapor-liquid equilibrium, vapor-liquid mass transfer and chemical kinetics. Hence, reactive distillation processes are highly nonlinear in nature. Application of reactive distillation for the production of ethyl acetate is considered for this dissertation. A detailed steady-state and dynamic mathematical model of reactive distillation is developed. This model is used for control studies of the reactive distillation column. Nonlinear Model Predictive Control algorithm is developed for centralized multivariable control of reactive distillation column. The performance of NLMPC is compared with decentralized PI control structure.