Multivariate modeling and optimization of the reverse cationic flotation process of iron ore using depressant-modified starch

Abstract

The iron ore has low quality to be directly used in steel production. Reverse cation flotation is one of the most used processes in the separation of quartz and particles smaller than 0.150 mm, and, therefore, can be used in the production of pellet feed. This project aimed at the application of modeling and optimization of flotation parameters (starch mass and time) to improve and enhance the separation and process conditions. The study of a starch-based depressant modified by different levels of temperature, starch mass, time, and mass of ammonium hydroxide, was conducted to reduce the surface tension of the water and with that, enhance the stability and formation of the nano and microbubbles. Both steps were modeled via the Central Composite Design (CCD) response surface array and the multivariate and multiobjective optimization using the Normal Boundary Intersection (NBI) algorithm. The iron concentration was 77.83 % under the optimized conditions with only 0.33 % of SiO2 content.