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By F. S. Hoseinian, D. Deglon, L. leal Filho, M. Safari, T. C. Souza Pinto

This paper investigates the effect of chemical and physical operational parameters on the reverse flotation of Timbopeba itabirite iron ore in a pilot-scale pneumatic flotation cell. Operational parameters include collector and depressant dosages, feed rate, superficial gas velocity and solids concentration. Results show that increasing collector dosage results in an increase in quartz recovery, particularly for the coarser quartz where recovery increases by nearly 30%. Increasing depressant dosage results in a decrease in both quartz recovery and hematite loss, for the coarser hematite particles. Increasing the feed rate has no effect on quartz recovery or hematite loss, suggesting that the pneumatic flotation cell is not residence time constrained. Increasing the superficial gas velocity results in an increase in quartz recovery to an optimum at a Jg of around 0.6 cm/s after which it decreases significantly, particularly for the coarser particles where recovery decreases by as much as 50%. Decreasing the solids concentration does not affect quartz recovery but results in significant decreases in hematite loss. Here, fine and coarse hematite loss decreases from around 25% to 12% and 10% to 2% respectively. Superficial gas velocity and solids concentration are operating parameters which have the largest effect on the flotation performance of the pneumatic flotation cell. Results suggest that this flotation cell should be operated at relatively low superficial gas velocities and solids concentrations. However, these are pilot-scale findings and would need to be tested at an industrial-scale where different operating conditions may be optimal. Keywords: Operational parameters, reverse froth flotation; entrainment; iron ore

Jan 1, 2020

By J Rutter, D Francois-Bongarcon, M Federov, N Odiar, M Scott

There are two fundamental methods for metalliferous sensing of primary crushed ore available for deployment at operations. These methods can be classed as surface measurement techniques (including XRF, LIBS, NIR, SWIR) or penetrative/semi-penetrative measurement techniques (including PGNAA, PFTNA, GAA, MR). While the individual sensing technologies exploit different physical phenomena to measure metal content, penetrative sensor measurements of grade are often regarded to be superior to surface measurement techniques due to the much larger sampling volume penetrative techniques provide. Surface sensing techniques often utilise simpler, established technologies that have considerable benefits in the speed of analysis, capital costs, ease of deployment, social license to operate and operational safety. This paper investigates whether surface sensing techniques achieve adequate sampling representativity of primary crushed rock mass to provide economic benefit over penetrative sensing technologies for bulk ore sorting applications. The paper details the methodology used to assess whether surface XRF measurements change throughout various stages of comminution, and increased surface exposure, from primary crushed ore (top size of -175mm) to finely crushed product for assay (top size -2mm). The methodology was applied to 2 ore mineralisation styles, across variable metals and grades, to identify any associated opportunities and limitations to surface sensing techniques. The results assist in developing a method to determine if surface sensing and measuring via XRF is an adequate representation of the rock mass for bulk ore sorting applications and identify any associated limitations in particle size distribution, mineralisation or natural deportment strength to guide suitable application of surface sensing techniques.

Nov 10, 2020

By R Knight

The spatial variability of geometallurgical characteristics in a mineral deposit can have significant impact on financial and mine planning, mineral processing, concentrate quality and tailings composition throughout the life of a project. These characteristics that may determine the success of a project are not readily apparent to geologists unless they are integrated with deposit geology and assay data, modelled in 3D and incorporated into resource block models.One of the primary objectives of geometallurgy is to integrate and incorporate various geologic, geotechnical and geometallurgical parameters into resource block models, which typically are limited to in situ tonnes and grade of economic metals. Critical attributes, such as concentration of deleterious elements that affect the project viability need to be identified and integrated into the resource block model early in the exploration stage to predict project profitability and address potential environmental impacts.Using data from a volcanogenic massive sulfide deposit we present an example of the integration of ore geochemistry (cadmium, arsenic and antimony) with the results of preliminary, bench-scale metallurgical test work in a 3D geometallurgical model that predicts subdomains of poor-quality concentrate and tailings containing environmentally-sensitive elements. This critical information was incorporated into the existing resource block model in order to inform both financial modelling and mine planning.This example demonstrates that geometallurgical data can be effectively integrated with geological and assay data using Leapfrog MiningTM software (Leapfrog Mining) early in the exploration stage of a project. Three-dimensional representations of geometallurgical data can be created directly using primary additive data, as well as from proxies of non-additive parameters. Resource block models can be updated continually with new information as the project passes from exploration through production. In addition, the software allows for 3D visualisation of the characteristics of the materials that will eventually constitute the mill concentrate, tailings, and waste. This study emphasises the critical importance of establishing a systematic geometallurgical approach early in the exploration stage that can be continuously updated with new information throughout the life of the project. In the example that we present, sulfide ore that had otherwise been identified as high-value (in the absence of geochemical and metallurgical data) turns out to be low-value ore due to concentrate penalties and increased environmental impact.

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