2026-10-12T22:46:50Z https://www.scidb.cn/oai

10.57760/sciencedb.42360 2026-07-09T14:58:23Z

2026-07-09 Adaptation Model of Reagent Adsorption and flotation concentration Recovery for Size-Classified Coal Slime under shear pulp conditioning doi:10.57760/sciencedb.42360 en  Efficient flotation conditioning is known to enhance the adsorption of flotation collector reagents on the surfaces of coal slime particles and improve flotation recovery rates. However, the complex particle size distribution of coal slime leads to significant variations in the motion behavior of fine coal slime particles, reagent adsorption characteristics, and flotation response within the pulp conditioning flow field. Conventional single-parameter pulp conditioning approaches fail to accommodate the nonlinear relationship between particle size and reagent adsorption efficiency, resulting in poor recovery of coarse particles and inadequate selectivity for fine particles. In this study, a solid-liquid two-phase flow model was employed to numerically simulate the flow field characteristics of five coal slime particle sizes—with equivalent diameters of 0.043, 0.064, 0.110, 0.239, and 0.500 mm—across rotational speeds ranging from 300 to 1800 rev/min. High-speed camera experiments were conducted, and the results revealed that the average particle velocity increased linearly with rotational speed, while turbulent kinetic energy and dissipation rate exhibited quadratic growth patterns. Under identical flow conditions, particle size was found to significantly influence collector reagent adsorption rates and flotation performance. Notably, particles with a diameter of 0.239 mm achieved optimal performance at 1200 rev/min, with reagent adsorption rate, clean coal yield, and ash content reaching 81.91%, 81.20%, and 7.50%, respectively. Higher speeds led to reagent desorption and reduced efficiency. For particles smaller than 0.075 mm, increasing the rotational speed to 1500 rev/min enhanced dispersion and improved separation, increasing the combustible recovery rates for 0.064 and 0.043 mm particles from 70.15% and 46.32% to 76.15% and 60.27%, respectively. Multiple linear regression analysis was used to establish particle size-specific predictive models that correlate flow field parameters with combustible recovery rates, enabling the optimization of conditioning environments for different particle fractions. Model validation demonstrated that R² values exceeded 0.97 and RMSE was below 0.83% across all particle sizes, confirming the robust predictive capability of the models. These findings provide a theoretical foundation for optimizing the classification conditioning process of fine coal slime. Coal slime flotation; Pulp conditioning; Particle size distribution; Reagent adsorption; Simulation; Adaptive model Youli Han wangtingdoing PUBLIC https://creativecommons.org/licenses/by-nc-nd/4.0/ dataset Science Data Bank