Efficient preparation of size-controlled sodium alginate microspheres via adaptive Bayesian optimization of the spray process

Abstract

Preparing sodium alginate (SA) microspheres via spray-precipitation challenges precise size control due to complex parameter coupling. We propose an intelligent framework integrating adaptive Bayesian optimization (BO) with microfluidic spraying to maximize the yield of 90–110 µm microspheres. Utilizing a Gaussian process regression model and Latin hypercube sampling, the framework demonstrated exceptional efficiency. Initializing with 10 prior data points achieved convergence in just 12 iterations, reducing iteration cost by 29.4% compared to using 5 priors. Under optimal conditions, the predicted target droplet proportion (18.60%) precisely matched experiments (18.34%), yielding 18.75% target-sized SA gel microspheres post-curing. Additionally, SHAP analysis revealed that gas and liquid pressures dominate size distribution, elucidating the physical mechanism behind multiple local optima via multi-feature compensation. This study provides an efficient, low-cost strategy for customizing polymer microspheres, establishing a robust machine-learning paradigm for optimizing complex multiphase flows.