7TH EUROPEAN COKE AND IRONMAKING CONGRESS - ECIC 2016

Understanding the characteristics of iron ore is paramount for predicting the performance of downstream processes. Whilst chemical assay and mineralogical assessments are routinely undertaken, the effects of textural information are often overlooked. For an industry striving to optimise productivity, an understanding of the impact of the textural characteristics of feed materials on process performance is critical. Rapid data collection and meaningful data sets can only be gathered effectively by an automated method. Optical image analysis (OIA) has traditionally been used for reliable identification of different iron oxides and oxyhydroxides in iron ore. The automated CSIRO optical image analysis system Mineral4/Recognition4 was created for rapid mineral and textural characterisation of iron ore providing identification of different minerals, as well as different morphologies of the same mineral. The technique has further been applied to processed iron ore products such as iron ore sinter to determine key parameters such as porosity, different morphologies of hematite (primary and secondary), and different morphologies of SFCA (silicoferrite of calcium and aluminium), the key bonding phase in sinter. Application of textural identification has recently been extended to coke characterisation where the Mineral4/Recognition4 software effectively distinguishes between IMDC (inert maceral derived components) and RMDC (reactive maceral derived components). Furthermore, the software gives comprehensive characterisation of porosity, IMDC, RMDC and the boundaries between IMDC and RMDC which includes parameters such as size distributions, wall thickness, roundness, ferret ratio, and abundances. Together with comprehensive image analysis, textural identification and improvement of mineral maps, the software has many unique features needed for iron ore research including characterisation of large objects like pellets and ore lumps; automated gangue and quartz identification; automated particle separation; multiple block imaging; multiple image set processing and on-line measurements. All these features make the Mineral4/Recognition4 OIA system a unique, reliable, industry/research focused tool for ore, sinter and coke characterisation.