Cavitation-enhanced carbonation for nano-ZnO synthesis via an ultrasonic–jet coupled reactor: Machine learning prediction and multi-objective optimization using a genetic algorithm

mass transfer, enhances nucleation density, and inhibits secondary agglomeration. Consequently, the synthesized product exhibits higher crystallinity, a well-developed mesoporous structure, and a narrower crystallite size distribution. The proposed machine-learning-assisted genetic algorithm optimization strategy successfully addresses the challenges associated with multivariable nonlinear coupling. This work demonstrates the potential of this strategy in complex chemical process intensification and provides a novel technical route and theoretical basis for the green, controllable, and efficient preparation of high-performance Nano-ZnO.