Studies of flotation kinetics are essential for understanding, predicting, and optimizing the selective recovery of minerals and metals through flotation. Recently, much effort has been made to use intrinsic ore properties to model flotation behavior. Particle-based characterization methods, e.g. SEM-based image analysis, have enabled much of this development. However, currently available methods for studies of flotation kinetics can not accommodate single-particle data, resulting in incomplete use of data that is readily available today. In this contribution, a method is introduced to apply kinetic flotation models to individual particles. This method, based on lasso-regularized multinomial logistic regression, allows for an in-depth understanding of particle flotation behavior as a function of all measured particle characteristics. With the proposed method, the joint influences of particle size, shape, as well as modal and surface compositions on the recovery of individual particles can be taken into unprecedented consideration. The results of the simulated particle behavior showed a very good agreement to the outcome of conventional empirical studies and follow well-described froth flotation recovery behavior.

浮选动力学研究对于理解、预测和优化通过浮选对矿物和金属的选择性回收至关重要。最近，已经做了很多努力来使用固有的矿石特性来模拟浮选行为。基于粒子的表征方法，例如基于 SEM 的图像分析，已经实现了大部分的发展。然而，目前可用的浮选动力学研究方法无法容纳单颗粒数据，导致无法完全使用当今易于获得的数据。在这篇文章中，介绍了一种将动力学浮选模型应用于单个颗粒的方法。该方法基于套索正则化多项逻辑回归，可以深入了解作为所有测量颗粒特征函数的颗粒浮选行为。使用所提出的方法，可以前所未有地考虑颗粒尺寸、形状以及模态和表面成分对单个颗粒回收率的联合影响。模拟颗粒行为的结果与传统经验研究的结果非常吻合，并遵循充分描述的泡沫浮选回收行为。