Controllable Microfluidic System through Intelligent Framework: Data-Driven Modeling, Machine Learning Energy Analysis, Comparative Multiobjective Optimization, and Experimental Study

Abstract

Intelligent microfluidics in nanoparticle synthesis embodies a comprehensive synergistic approach that merges numerical modeling, artificial intelligence, and experimental analysis, striving for controllability over an energyefficient microfluidic device designed for nanoparticle synthesis with desired physical properties. This study delves into a microfluidic mass transfer system, employing an innovative methodology that combines data-driven modeling, machine learning-based comparative multiobjective optimization, and experimental analysis to model a micromixing system. A surrogate data-driven model is employed to the microfluidic mass transfer system, considering four critical geometrical parameters and inlet Reynolds as design variables. The model provides insights into mixer's functionality. It is observed that at lower Reynolds numbers, increasing NoT increases the mixing efficiency by more than 20%. Moreover, altering SND i value leads to a significant 80% reduction in pressure drop. Identifying the optimal system from numerous design parameters is challenging but accomplished through machine learning. Two distinct machine learning algorithms were integrated with mathematical surrogate modeling to optimize the mixer for three objectives. RSM-Differential Evolution significantly outperforms RSM-NSGA-II in enhancing mixing characteristics and reducing the mechanical energy consumption by over 85%. Additionally, improvement in energy dissipation and effective energy efficiency of microsystem was made, alongside a comparable enhancement of mixing index and management of pressure drop. Fabrication of two optimal designs confirms an over 80% drop in pressure and an increase in mixing efficiency by over 20% at low Reynolds, outperforming conventional microfluidic mixers. The intelligent micromixer allows precise control over nanoparticle synthesis by adjusting microtransfer design parameters. This controlled process is crucial for tissue engineering hydrogel synthesis, nanotechnology, and targeted drug delivery.