TY - JOUR
T1 - Intelligent control strategy for electrified pressure-swing distillation processes using artificial neural networks-based composition controllers
AU - Yang, Daye
AU - Wang, Jingcheng
AU - Cai, Huihuang
AU - Rao, Jun
AU - Cui, Chengtian
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - Artificial neural networks
KW - Dynamics and control
KW - Electrically-driven process
KW - Intelligent composition control
KW - Pressure-swing distillation
UR - http://www.scopus.com/inward/record.url?scp=85211985731&partnerID=8YFLogxK
U2 - 10.1016/j.seppur.2024.130991
DO - 10.1016/j.seppur.2024.130991
M3 - Article
AN - SCOPUS:85211985731
SN - 1383-5866
VL - 360
JO - Separation and Purification Technology
JF - Separation and Purification Technology
M1 - 130991
ER -