Machine Learning-Based Impact of Rotational Speed on Mixing, Mass Transfer, and Flow Parameter Prediction in Solid–Liquid Stirred Tanks

To address the issues of low solid–liquid mixing and mass transfer efficiency and difficult real-time regulation in the resource utilization of non-ferrous metal smelting slag, this study constructs a research framework integrating Computational Fluid Dynamics-Discrete Element Method (CFD-DEM) coupling models and machine learning. The framework systematically investigates particle motion characteristics and mass transfer laws in stirred tanks and enables an intelligent prediction of key parameters. Through a CFD-DEM two-way coupling simulation, the study quantifies particle dispersion characteristics using relative standard deviation (RSD) and calculates the mass transfer coefficient (k) based on the Hughmark model, revealing the effects of particle size and impeller speed on mixing and mass transfer efficiency. For parameter prediction, particle motion and mass transfer data are used to train a multi-model prediction library, with model performance evaluated through comparative experiments. The results show that increasing the rotational speed shortens the particle mixing time, reduces RSD values by 25–40%, increases the coupling force, and decreases stability during the circulation phase. Different machine learning (ML) algorithms exhibit varying performances in the time-series prediction of particle motion characteristics and real-time prediction of mass transfer coefficients. Notably, GA-BP achieves a fitting degree R of 0.99 in both predictions, meeting the requirements for the structural optimization and intelligent regulation of stirred tanks. This research provides theoretical support and technical pathways for the structural optimization and intelligent control of stirred tanks, offering engineering application value in fields such as hydrometallurgy and solid waste resource utilization.