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Abstract
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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 𝑋𝐷1,𝐴𝐶𝐸 and 𝑋𝐷2,𝐶𝐻𝐿 are 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.
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