Characterising the Blasting Properties of Iron Ore. Andrew Scott Scott Mine Consulting Services Pty Ltd 14 th July, 2015

Transcription

1 Characterising the Blasting Properties of Iron Ore Andrew Scott Scott Mine Consulting Services Pty Ltd 14 th July, 2015

2 What is it all about? Why predict blast fragmentation? How to predict blast fragmentation The role of rock mass properties in blast design in fragmentation modelling What is different about iron ore? Blasting properties of iron ore Treating iron ore as a mixture Sources of data Greenfield case study.

3 How are blasts designed? Explosive Density Rock Factor Early blast design rules targeted satisfactory blast performance, including fragmentation Diameter Burden There are myriad qualitative blast design rules that influence the geometry of a design Production blasts are usually designed as simple variants of the previous blast with changes made on a qualitative basis. Hole Length Stemming Burden Bench Height Free Face B B Free Face S The rock mass has little prominence in most of these design rules Sub-drill

4 How are blasts designed? Explosive Density Rock Factor Early blast design rules targeted satisfactory blast performance, including fragmentation Diameter Burden There are myriad qualitative blast design rules that influence the geometry of a design Production blasts are usually designed as simple variants of the previous blast with changes made on a qualitative basis. Hole Length Stemming Burden Bench Height Free Face B B Free Face S The rock mass has little prominence in most of these design rules Sub-drill

5 How to predict blast fragmentation We need a model!

6 How to predict blast fragmentation We need a model! A model is a quantitative framework that presents a simplified version of reality that allows the relationships between cause and effect to be understood Two development paths An empirical or engineering approach A mechanistic or fundamental approach

7 Mechanistic approaches Attempt to simulate the dynamic fracture processes Fracture mechanics, hydro dynamics, physics, chemistry,..sophistry.. Data describing dynamic rock behavior requires sophisticated tests Computing requirements are intensive and it is not yet possible to routinely model practical field problems using these tools. Onederra et al (2010)

8 A successful example the Kuz Ram Model Rock Factor Charge Weight Powder Factor Mean Fragment Size Relative Explosive Strength 1.. Rosin Rammler Equation

9 Development of empirical fragmentation models

10 The blasting properties of a rock mass Strength Factor Properties affecting blastability Strength how difficult is it to break the rock? Density relationship between volume and mass. Structure how broken is it already? Stiffness, porosity, energy breakage relationships, moisture content, etc, etc Generally combined into an index or rock factor Density Factor Structure Factor Unconfined Compressive Strength - MPa Strength Fractures per metre Structure Density t/bcm Density

11 What is different about Iron Ore? Traditional blasting research has focused on hard, competent rock more like magnetites than traditional WA iron ores These ores are highly variable in terms of strength, structure and density exactly the properties that affect blasting performance! This variability is not just between types, but can also occur within a mine bench!

12 What is different about Iron Ore? Traditional blasting research has focused on hard, competent rock more like magnetites than traditional WA iron ores These ores are highly variable in terms of strength, structure and density exactly the properties that affect blasting performance! This variability is not just between types, but can also occur within a mine bench!

13 Blasting Properties of Iron Ores Type Strength (MPa) Fracture Frequency Density t/bcm Rock Factor Magnetite Massive Haematite Blocky Haematite Banded Iron Haematite / Goethite Goethite / Limonite

14 Treated as a single species If fired with a single blast design: 12 m bench, 251 mm dia. blast holes 7.0 x 7.9 m pattern Heavy ANFO explosive 0.73 kg/bcm powder factor

15 Treated as a mixture Examination suggests that many ores are made up of separate components or mixtures. It is possible to identify: A host matrix or continuous phase A hard component A proportion of in situ fines It is possible to identify these components in core and allocate blasting properties to each. In the field the properties of the individual components do not appear to change significantly within the one ore type, but their proportion may change significantly.

16 Mixtures X80 Millimetres Millimetres Heamatite Heam / Geot Geot / Limon X Heamatite Heam / Geot Geot / Limon Fines 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% Heamatite Heam / Geot Geot / Limon Single Mixture Single Mixture Single Mixture Blocky Haematite Matrix Hards Fines Proportion 47.5% 47.5% 5% Density Strength Fracture Frequency 5 3 ROM Size Boulders X80 X50 Fines Previous Analysis mm 213 mm 12% Component Model mm 130 mm 23% Haematite / Goethite Matrix Hards Fines Proportion 30% 60% 10% Density Strength Fracture Frequency 10 5 ROM Size Boulders X80 X50 Fines Previous Analysis mm 174 mm 17% Component Model mm 74 mm 33% Goethite / Limonite Matrix Hards Fines Proportion 52.2% 32.5% 15% Density Strength Fracture Frequency ROM Size Boulders X80 X50 Fines Previous Analysis mm 53 mm 32% Component Model mm 30 mm 44%

17 Sources of Data Drill Core Strength indices UCS / stiffness PLS Sonic velocities Breakage parameters Structure RQD FF Geotechnical logging Density Intact Porosity

18 Sources of Data Active Pits Monitor Drill Performance Face Sampling and Mapping Logging Drill Cuttings Surface and down hole geophysics Ramos, Hatherly and Montiero, ACFR, 2009 Performance in the adjacent block and updated geological models

19 Practical Example Greenfield Site Form Weathering Proportion Strength MPa Fracture Frequency Density (t/bcm) Massive Fresh 14% Blocky Fresh 58% Broken 28% Core from a 12 m zone from a channel iron deposit

20 Conclusions Some quite useful blasting models exist All models require appropriate data if they are to generate useful predictions Most iron ores occur as complex mixtures of lithologies and properties The components of these mixtures are often of consistent character within a blasting domain, but vary in their relative proportions The blasting characteristics of each component of the mixture can be quantified The overall fragmentation result can be generated from the weighted average of the fragmentation achieved for each component. Automated data collection, blast design and field implementation remains an alluring challenge!