Computing single-particle flotation kinetics using automated mineralogy data and machine learning

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.