PID Controller Tuning Optimization Using Genetic Algorithm for Droplet Size Control in Microfluidics

Gyimah, Nafisat; Joemaa, Rauno; Pärnamets, Kaiser; Scheler, Ott; Rang, Toomas; Pardy, Tamás

doi:10.1109/bec56180.2022.9935596

Cited by 1 publication

(3 citation statements)

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“…In reference to our previous work, the PID controller parameters (i.e., proportional gain, integral gain, and derivative gain) were derived using a Genetic Algorithm (GA). The dual-PID control strategy, adapting the framework implemented with MATLAB, Simulink in [24], was modified to include droplet generation frequency control for the presented version of the droplet microfluidics system, using Python.…”

Section: B Electronics Module 1) Flow Control Strategymentioning

confidence: 99%

“…To tune the PID gains, experimental data (from our previous works [23], [24]) demonstrating the effect of inlet pressure on droplet size were used in MATLAB to derive pumping system component transfer functions. The transfer functions were implemented in Simulink in a setup-derived closed loop feedback model.…”

Section: ) Pressure-based System Feedback Modellingmentioning

confidence: 99%

“…Based on our previous works [23], [24], the system components were mathematically represented as transfer functions in eqns. 1-3, using the collected data in MATLAB software.…”

Section: A Submodule Evaluation 1) Pressure-based System Feedback Mod...mentioning

confidence: 99%

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CogniFlow-Drop: Integrated Modular System for Automated Generation of Droplets in Microfluidic Applications

Jõemaa,

Gyimah,

Ashraf

et al. 2023

IEEE Access

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Droplet microfluidics enables studying large cell populations in chemical isolation, at a singlecell resolution. Applications include studying cellular response to drugs, cell-to-cell interaction studies. Such applications need a reliable and repeatable droplet generation with high monodispersity. Most systems used in research rely on manual tuning of flow parameters on off-the-shelf instruments. Setups are highly customized, limiting reproduction of experimental results. We propose an integrated, modular system for automated aqueous droplet generation with high monodispersity. The system provides dynamic feedback control of droplet size and input pressure. Input pressure is generated by two piezoelectric micropumps. Droplet sizes are determined via light intensity measurement in an LED-photodiode setup. The system is capable of wireless communication and has a low enough power consumption for battery-powered operation. We report on the assembly and the underlying working principle, as well as an in-depth experimental evaluation of the performance of the proof-of-concept prototype in aqueous droplet generation. Evaluation was performed on a modular as well as on a system level. During module-level evaluations, aqueous droplets were generated in a light mineral oil + Span 80 surfactant carrier medium, using 3 different flow-focusing junction geometries. The presented prototype had a significantly faster pressure stabilization time (10 s) compared to a syringe pump-based reference setup (120 s). During system-level evaluation, deionized water droplets were generated in a carrier medium of HFE7500 + PEG-PFPE triblock surfactant. Resultant droplet sizes were benchmarked with microscopy. The system was able to repeatedly generate mono-and polydisperse droplets on demand, with CVs between 5-10% in the ~50-200 m droplet diameter range.

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Section: B Electronics Module 1) Flow Control Strategymentioning

confidence: 99%

Section: ) Pressure-based System Feedback Modellingmentioning

confidence: 99%