Quantitative Estimation of Mineral Phases from Chemical Assays and Powder X-Ray Diffraction Rietveld Analysis: A Case Study on Selective Flocculation of Iron Ore Slimes

A generic method for quantitative estimation of mineral phases in ores and process streams has been illustrated in this paper with the help of an example taken from our own work on the selective flocculation of alumina rich iron ore slimes. The method used conventional bulk chemical assay data and information on phases present, as determined by powder X-ray diffraction (XRD) pattern analysis. Iron ore slime samples were subjected to selective dispersion–flocculation experiments and the feed, concentrate and tails were analyzed using this method. Chemical assays (iron, alumina, silica and loss on ignition) were determined by wet chemical analysis and mineral phases (hematite, goethite, gibbsite, kaolinite and quartz) were quantified by Rietveld analysis of XRD patterns. Considering the proportion of various elements present in the constituent minerals, the chemical assay data and the reconciliation of assays (as well as the mineral compositions determined by Rietveld analysis, if available), one could estimate the proportion of various minerals present in different process streams. The mineral compositions estimated based on this method matched reasonably well with those obtained by quantitative phase analysis by the Rietveld analysis. In case the Rietveld analysis results were also reconciled with the elemental assays, one could arrive at even a better estimate of the proportion of various minerals in the different process streams. The estimated mineral compositions in feed, concentrate and tails were also used to compute mineral wise recoveries. The approach presented in this paper would provide mineral engineers a simple and relatively easy method to convert elemental assays into mineralogical compositions and thus compute extremely useful and reliable estimates of the mineral-wise recoveries, grades and distributions in the various plant streams, which are necessary for plant optimization and control purposes.