Iron Ore Beneficiation Africa

Modelling of Physical Separation Processes of fine ores

Carl Bergmann 17 March 2014

Agenda

Objectives Using Particle Tracking Analysis – Method Using Particle Tracking Analysis – Results Flowsheet Simulation Conclusions

Objectives

FINE ORE CHARACTERISATION

How to characterize a fines sample in terms of size, density, shape, liberation, grade?

Size and shape – laser scattering, cyclosizer, screening, mineralogical

Density – particle pycnometry, heavy liquids, mineralogical

Liberation – grain free surface area, volume percent, mass percent

Objectives FLOWSHEET MODELLING

How to model the performance of fines separators in terms of size, density, shape liberation and

grade?

Milling WHIMS Cyclone

Flotation

Spirals

Screening

Ore characterization - method

A sample of fine hematite ore (-1mm) was characterized using Particle Tracking Analysis (PTA)

•  XRD and SEM were used to identify 12 different minerals in the feed sample

•  These minerals were analysed and assigned chemical compositions using –  Microprobe (on individual mineral types) –  ICP chemical assay (on sized and spiral products) –  Standard mineralogical compositions (Quartz = SiO2)

•  A relative density was assigned to each mineral

Ore characterization - method

Two types of hematite were identified: •  Clean hematite with composition close to theoretical Fe2O3 •  Hematite with ultra-fine inclusions of silicates

Mineral list

Ore characterization - method

•  Approximately 124 000 particles were mapped using AutoSEM. •  Data exported as Excel files including: mass, perimeter, area,

assay composition, density, shape parameters, state of liberation of each grain.

Ore characterization - method

•  Data sorted into 10 size classes and 14 density classes •  Choose the range of each size class •  Choose each density interval to match identified minerals

Ore characterization - method What particle size should be used?

Particle Morphology Circularity and Shape factor distributions for each mineral can be measured

Ore Characterization - method Factors that influence PTA reliability

•  Stereological Effect – 2D sections of 3D reality – traditionally overestimates liberation •  Sufficient data to reliably represent ‘reality’ – 10% mineral vs 0.1% mineral

–  100 000 particles –  1 000 000 particles?

•  Number of coarse particles •  Mineral surface roughness •  Porosity

Perhaps 3D tomography in the future

Ore Characterization - results

Feed sample - Mineral Content

Ore Characterization - results

Total feed – Size x Density

Hematite grains – Size x Density

Ore characterization - results

Feed sample - Size x Grade

Ore characterization - results

Feed sample - Density x Grade

Ore characterization - results

Ore characterization - results

Ore characterization - results

Ore characterization - results

Ore characterization - results

Circuit simulation

Circuit simulation

Partition Surfaces •  The performance of each process unit must be modelled in

terms of both size separation and density separation •  These partition surfaces are generated experimentally by taking

multiple product and tailings samples at various mass yields

Mill breakage rates per mineral type can also be determined

Circuit simulation

Circuit simulation

Circuit simulation

Circuit simulation

Cyclone performance data

Circuit simulation

Circuit simulation

Spiral 1 performance data

Circuit simulation

Circuit simulation

Spiral 2 performance data

Circuit simulation

Mill performance – no liberation

Mill performance – total liberation

Circuit simulation

Circuit simulation

Input overall circuit data

Circuit simulation

•  Opportunity to optimize product grade/recovery with

changing grind size

•  Full grade recovery curves can be constructed for each

process unit

•  Distinction is made between middlings type – misplaced or

locked particles

•  Opportunity to refine unit models over time

•  Can include the effect of particle shape

Conclusions

Automated SEM with PTA can be used to characterize fine ore samples in terms of size, density, shape, liberation and composition

The performance of gravity/size separation units at varying yields can be modelled using the PTA data and partition surface models

Simple flowsheets including recycle streams can be simulated

Recommend that this technique be expanded to model other processes such as magnetic separation and flotation

Refine technique for low grade ores (particle count)