Design of adaptive-PID-Smith control in the two-mixing tank series

ABSTRACT

A control strategy must be developed to maintain the product composition of the two- or more-mixed-flow reactor series. In this research, a two-mixing tank series (TMTS) was utilized in the laboratory to approach the two-mixed flow reactors’ problems. Both tanks are designed to overflow so that the volume remains constant. The salt solution was directly fed to Tank 1, while the water was charged to both tanks as the mixing progressed. The output of Tank-1 flowed to Tank-2 through a rather long pipe, resulting in dead time in Tank-2. The Smith predictor was applied to overcome dead time. The PRC (process reaction curve) method was used to tune the PID (proportional-integral-derivative) parameters of Tank-1 and Tank-2. The novel composition controls, adaptive-PID (APID) and modified metaheuristic adaptive-PID (MMAPID), have been proposed and compared with conventional PID. The mathematical models were rigorously examined through XCOS simulations. The processing system is considered sensitive to changes in input disturbances since the process time constants of both tanks were quite low. The dead time of 0.5 minutes was found in Tank 2. The PID parameters produced by PRC experiments resulted in stable responses to changes in the disturbance system. As shown in the dynamic simulation study, both novel APID and MMAPID give almost the same results; their responses are faster than the conventional PID. Based on the integral absolute error (IAE) results, APID with scheduling PID parameters produced the smallest IAE and outperformed the conventional PID. The results of the study highlight APID and MMAPID potential in PID control applications for composition control in a TMTS. The combined APID-Smith and MMAPID-Smith were able to overcome the dead time well. This study creates opportunities for engineers and professionals to use cutting-edge control technologies, improving automation and chemical process industries.

Graphical Abstract

KEYWORDS:

• Adaptive
• metaheuristic
• pid
• tmts
• xcos

Acknowledgments

We are grateful for the funding provided for this research by LPPM UPN “Veteran” Yogyakarta. It’s incredibly helpful to have technical support for using the free software in XCOS and Scilab.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplemental data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/23080477.2024.2350818.