Model predictive control of pressure-swing distillation via closed-loop system identification

Pressure-swing distillation (PSD) is a proven technique for separating azeotropic mixtures by exploiting pressure-dependent shifts in azeotropic composition. Despite its efficacy, PSD systems present significant control challenges due to inherent nonlinearities, complex multivariable interactions, and internal recycle loops. This study proposes a model predictive control (MPC) framework for PSD systems, founded on closed-loop system identification. A comprehensive plantwide nonlinear dynamic model of a PSD process for separating a maximum-boiling azeotrope of acetone and chloroform is developed using Aspen Dynamics and interfaced with MATLAB/Simulink for controller design and testing. To address the limitations of open-loop excitation in systems with recycles, pseudo-random binary sequence (PRBS) signals are applied under closed-loop operation to sufficiently excite the process. Subsequently, linear state-space models are identified using the prediction error method. Based on these models, two MPC configurations are developed: temperature control (TC) and composition–temperature cascade control (CC–TC). Simulation results demonstrate that the proposed MPC strategies quantitatively outperform proportional–integral (PI) controllers. Specifically, under the TC strategy, the total integral of absolute error (IAE) values of X _D1,ACEand X _D2,CHLare reduced by approximately 10% and 3%, respectively; while under the CC–TC strategy, the reductions reach about 26% and 55%. Moreover, across four disturbance scenarios, the steady convergence times of both composition purities are shortened by more than 5 h compared with PI controllers. These results highlight the advantages of the proposed MPC strategies in disturbance rejection and transient product quality regulation. These findings underscore the effectiveness of closed-loop system identification as a basis for advanced control of PSD processes.