The transformations of low-grade manganese ore were investigated during roasting in the air at different temperatures up to 1200 degrees C. The transformations were followed up by XRD and TGA-DTA. Moreover, the morphology and magnetic properties were determined by SEM and VSM. It was observed that MnO2 transformed to the lower oxide Mn5O8 at 500 degrees C and then to bixbyite (Mn2O3) at 600 degrees C. Finally, the bixbyite decomposed to hausmannite (Mn3O4) at 800 degrees C. Increasing the roasting temperature to 900 degrees C induced a reaction between hematite and hausmannite and led to the formation of a small amount of solid solution of the ferrite spinel MnFe2O4. Further increase in temperature to 1000 degrees C led to the formation of a solid solution of braunite (Mn7SiO12) which decomposed to rhodonite (MnSiO3) at 1200 degrees C. The magnetic susceptibility of the original ore gradually increased with the roasting temperature, from 0.119 x 10(-3) at ambient temperature to a maximum value of 80 x 10(-3) at 1200 degrees C.
The feed of mineral processing plants, usually consist of different minerals from various geological zones, which show different behavior in separation processes. In this research, samples from supergene and hypogene zones were provided to investigate the flotation behavior of copper minerals. Flotation experiments were carried out in three phases of supergene sample, hypogene sample and mixed samples. Based on the results, the recovery rate of the mixed sample was 83.61%, which is 7.63% and 1.79% higher than the recovery of the samples of hypogene and supergene zones, respectively. The concentrate grade values obtained for blended, hypogene zone and supergene zone are 10.32%, 2.81% and 12.37%, respectively. The maximum values of flotation constant and infinite recovery are 0.956 (s−1) and 88.833% for the mixed sample. It was also concluded that the highest amount of k and infinitive recovery were related to supergene zone sulfide flotation which are 0.831 (s−1) and 84.33% respectively.
Mineral processing simulation models can be classified based on the level that feed stream to the plant and unit models are described. The levels of modelling in this context are: bulk, mineral or element by size, and particle. Particle level modelling and simulation utilises liberation data in the feed stream and is more sensitive to the variations in ore quality, specifically ore texture. In this paper, simulations for two texturally different magnetite ores are demonstrated at different modelling levels. The model parameters were calibrated for current run-of-mine ore and then in the simulation applied directly to the other ore. For the second ore, the simulation results vary between the different levels. This is because, at the bulk level, the model assumes minerals do not change their behaviour if ore texture or grinding fineness are changed. At the mineral by size level, the assumption is that minerals behave identically in each size fraction even if the ore texture changes. At the particle level, the assumption is that similar particles behave in the same way. The particle level approach gives results that are more realistic and it can be used in optimisation, thus finding the most optimal processing way for different geometallurgical domains.
Reprocessing of iron ore tailings (IOTs) and extracting recoverable valuable iron oxides will become increasingly financially attractive for mining companies and also may reduce environmental problems. Using databases built based on long term monitoring of units installed on plants to control the operational conditions to generate artificial intelligence models can decrease the cost of reprocessing operations Although some investigations have been focused on the reprocessing of IOTs, several challenges still remain which need to be addressed, especially for fine particles. SLon®, has developed a pulsating high gradient magnetic separator for the processing of fine iron oxides. However, there has been no systematic optimisation and variable assessments for SLon® operating variables to examine their effects on metallurgical responses (separation efficiency) on the industrial scale. This study addressed these drawbacks by linear (Pearson correlation) and non-linear (random forest) variable importance measurements (VIM) through an industrial SLon® installation.