Intelligent control strategy for electrified pressure-swing distillation processes using artificial neural networks-based composition controllers
Daye Yang (Shanghai Jiao Tong University)
Jingcheng Wang (Shanghai Jiao Tong University)
Huihuang Cai (Shanghai Jiao Tong University)
Jun Rao (Shanghai Jiao Tong University)
C. Cui (TU Delft - ChemE/Product and Process Engineering)
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Abstract
This study introduces a novel artificial neural network (ANN)-based control strategy for pressure-swing distillation (PSD) systems, integrating heat pump-assisted distillation (HPAD) and self-heat recuperation technology (SHRT) to transition from thermally-driven to electrically-driven processes. While previous research has validated the dynamics and controllability of conventional PSD (PSD-CONV), PSD-HPAD, and PSD-SHRT for separating a maximum-boiling acetone/chloroform azeotrope, this work specifically focuses on enhancing product purity control through composition-temperature cascade control (CC-TC). Although similar control strategies have been proposed, our approach uniquely predicts temperature set points using easily measurable process variables, effectively bypassing the inaccuracies of composition measurements. Simulation results demonstrate that this ANN-based strategy significantly improves dynamic performance and adaptability in controlling product purity without requiring a composition analyzer. By leveraging the strengths of traditional Proportional-Integral-Derivative (PID) control alongside data-driven methods, this research highlights a critical advancement in the control of electrified PSD applications, paving the way for more efficient and reliable distillation processes.
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