Artificial intelligence-based droplet size prediction for microfluidic system
International Journal of Numerical Methods for Heat & Fluid Flow
ISSN: 0961-5539
Article publication date: 15 August 2024
Issue publication date: 2 September 2024
Abstract
Purpose
This study aims to introduce a vision-based model to generate droplets with auto-tuned parameters. The model can auto-adjust the inherent uncertainties and errors involved with the fabrication and operating parameters in microfluidic platform, attaining precise size and frequency of droplet generation.
Design/methodology/approach
The photolithography method is utilized to prepare the microfluidic devices used in this study, and various experiments are conducted at various flow-rate and viscosity ratios. Data for droplet shape is collected to train the artificial intelligence (AI) models.
Findings
Growth phase of droplets demonstrated a unique spring back effect in droplet size. The fully developed droplet sizes in the microchannel were modeled using least absolute shrinkage and selection operators (LASSO) regression model, Gaussian support vector machine (SVM), long short term memory (LSTM) and deep neural network models. Mean absolute percentage error (MAPE) of 0.05 and R2 = 0.93 were obtained with a deep neural network model on untrained flow data. The shape parameters of the droplets are affected by several uncontrolled parameters. These parameters are instinctively captured in the model.
Originality/value
Experimental data set is generated for varying viscosity values and flow rates. The variation of flow rate of continuous phase is observed here instead of dispersed phase. An automated computation routine is developed to read the droplet shape parameters considering the transient growth phase of droplets. The droplet size data is used to build and compare various AI models for predicting droplet sizes. A predictive model is developed, which is ready for automated closed loop control of the droplet generation.
Keywords
Acknowledgements
Funding: The project work was sponsored by the “National Supercomputing mission (NSM)”, India (Grant-I Number: DST/NSM/R&D_HPC_Applications/2021/08, Grant-II Number: DST/NSM/R&D_HPC_Applications/Extension Grant/2023/04).
Conflicts of interest: The authors declare no conflict of interest.
Ethics statement: This study had no direct or indirect involvement of human subjects, human data, human tissue or animals.
Citation
Dubey, S., Vishwakarma, P., Ramarao, T., Dubey, S.K., Goel, S. and Javed, A. (2024), "Artificial intelligence-based droplet size prediction for microfluidic system", International Journal of Numerical Methods for Heat & Fluid Flow, Vol. 34 No. 8, pp. 3045-3078. https://doi.org/10.1108/HFF-07-2023-0361
Publisher
:Emerald Publishing Limited
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