LAUSR

Abstract With the development and improvement of solvents (or entrainers), the extractive distillation process has been significantly enhanced, and the energy consumption of the separation process has been effectively reduced. In this work, the energy-efficient separation process for extractive distillation with deep eutectic solvent ChCl/Urea (1:2) is designed. The ethanol dehydration process is selected as a case study to evaluate the practical utility of this novel solvent in large-scale production. The multi-objective genetic algorithm is used to optimize the extractive distillation process, and the waste heat is recovered through the heat intensification strategy to achieve the optimal design. Five control schemes, which include proportional-integral (PI) and model predictive control strategies (MPC), are developed for the proposed separation process. According to the nonlinear relationship between manipulated variables and controlled variables, the improved control structures are designed to enhance the dynamic performance. Integral absolute error (IAE) and destroyed exergy are applied as evaluation criteria to test the controllability and energy efficiency of the system. The results show that both CS4 and MPC control schemes have good controllability, and the MPC control scheme displays better energy efficiency than the CS4 control scheme.