Purity control of simulated moving bed based on advanced fuzzy II controller

• Published in: PloS one Vol. 20; no. 2; p. e0314545
• Main Authors: Xie, Chao-Fan, Lin, Ting, Zhang, Hong
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 24.02.2025; Public Library of Science (PLoS)
• Subjects: Accuracy; Adsorption; Algorithms; Artificial intelligence; Chromatography; Computer and Information Sciences; Computer Simulation; Control; Controllers; Engineering and Technology; Equilibrium; Experiments; Fuzzy control; Fuzzy Logic; Mathematical models; Methods; Models, Theoretical; Moving beds; Parameter estimation; Parameter sensitivity; Partial differential equations; Physical Sciences; Research and Analysis Methods; Separation; Stability analysis; Steady state; System theory; Variables; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0314545

Simulated Moving Bed (SMB) is the optimal technology for chromatographic separation, but its process is complex and sensitive to numerous parameters that affect separation performance, making it difficult to control. In recent years, fuzzy controllers have been widely applied in industry due to their simplicity, robustness, and ease of implementation. However, traditional fuzzy controllers used in industry do not consider the error acceleration term. In steady-state conditions, error acceleration is typically slightly less than the target value. Introducing the acceleration term, albeit non-fuzzy, in a proactive fuzzy I-type controller often leads to an increase in steady-state values. The study shows that, compared to the advanced fuzzy I-type controller, the extraction accuracy for material B improved by an average of 0.7%, while the accuracy for material A increased by 0.1%. Compared to traditional fuzzy controllers, the extraction accuracy for material B improved by an average of 0.35%, while the accuracy for material A remained relatively stable. In terms of stability analysis concerning variations in moving bed parameters, the advanced fuzzy II-type controller exhibited greater stability than the I-type, with an average precision stability improvement of 0.6%. Traditional fuzzy controllers demonstrated pathological characteristics during fluctuations in the switching time parameter, whereas the advanced fuzzy II type controller-maintained stability.