To take a giant leap forward in ore control, a sophisticated approach to post-blast ore control is required. It must incorporate a ‘smart’ vector-field, heave knowledge, vectors of displacement, in-situ geology, and production opportunities. Based on dozens of studies, the gain is very-much worth the effort.
The effects of blast movement on surface hard rock mines are well-documented. Case studies show that measuring blast movement is far superior to modelling due to variable movement and the inability to predict displacement with accuracy (Hunt, W and Thornton, D, 2013). Additionally, many case studies show large value in mining moved ore blocks instead of in-situ ore blocks (M Fitzgerald, 2011).
The value calculated by these studies typically treat movement as a 2D problem, with benefits quantified by simple 2D area measurements (Figure 1).
The reality is that the situation is not as simple as two-dimensional analysis can calculate. However, tools didn’t exist to properly evaluate the problem in 3D until recently, so this was the best estimate of value that could be offered at the time. Using this method allowed users to give an indication as to the value of movement knowledge (Loeb, 2014).
Research into three-dimensional movement benefits has been far less frequent, in part because measuring vertical movement below the surface of muck piles is difficult, and the industry has primarily relied upon two-dimensional polygons to control ore. Even in the case of multi-pass or flitch mining, the financial benefits of mitigating vertical movement have been difficult to measure, and creating systems to control ore in three-dimensions have been elusive (Hunt, W, 2015).
Preliminary results from research, and feedback from mines who were consistently measuring blast movement, but not seeing the benefits predicted by 2D methods, showed large potential gains in optimizing ore block creation in 3D. However, the solution to the problem requires a different approach to the standard methodology used by mines since blast movement was first defined (Thornton, DM, 2009).
To find a solution for the 3D movement problem, the authors partnered with several major mining companies, including the operator of a narrow vein high-grade gold mine. Ore in this operation is mined using excavators and processed through a SAG mill. Conventional open pit bulk mining ore control methods are utilized; ore and waste are blasted jointly, polygons are generated using blast hole data and selectively separated during mining under the visual guidance of mine geology. Benches are either five or ten meters and mined in two flitches.
The mine had been using a blast movement monitoring system for years to track blast-induced ore movement. The pre-blast ore blocks were translated in 2D along a horizontal plane using measured vectors, as has been the industry-standard process for many years. The shape of the pre-blast ore block is usually kept intact during the 2D translation, with some stretching in backline areas, and some ore blocks shrinking during convergence.
Locally, structure/lithologic controls, in addition to variance in drilling, loading, water content, explosive quality, and stemming height, create variable blasting conditions which are unfavorable to consistent movement. It has been observed while using conventional blast movement methods, the integrity of the polygon is not preserved under these conditions as differential movement alters the shape of the ore body. Figure 2 is a cross-sectional representation of measured movement and estimated ore block translation in 3D in a monitored blast.
Under these circumstances, the use of the polygon generated prior to blasting will provide a lower economic benefit than originally intended. The polygon must be adjusted to account for differential movement to achieve desired ore quality. The adjustment of the post-blast polygon is no longer a geologic interpretation of the ore body, rather, an exercise in maximizing value based on new parameters realized post-blast.
For example, consider Figure 3, which is the ore structure from Figure 2 post-blast. Should the mine pull the lines inward and lose ore, or should they expand the lines and take some dilution? The answer is that it depends on the value of the ore, the value of the waste, the slope of the working face (short flitches can be mined at near 90 degrees, but taller working faces result in mining face angles of near 60 degrees). This calculation cannot be performed by a person, as it requires billions of calculations over an entire blast volume.
After refining the blast movement monitoring process over many months, the mine in question began to look at the 3D movement as a major opportunity to optimize ore control and value creation, in addition to reducing unintended dilution and maximizing use of the ore body.
From the outset, it was understood that there would be many challenges to creating a solution for this problem, and that it would take significant effort to implement. To produce a software product that solved this complex problem, several questions had to be answered. Some of them were:
- “How do we optimize a 2D method in 3D?”
a. How can we take a 2D ore block and contour it in 3D?
- “How can we confidently take discreet movement vectors and translate data in 3D?”
a. It isn’t possible to install thousands of movement monitors.
- “How do we address differential movement in 3D?”
a. The pre-blast ore shapes will distort when moved, if moved accurately.
- “Once we are confident as to the post-blast shape of the ore, how do we optimize?”
a. Optimizing a blast in 2D is challenging, but optimizing in 3D is an exponentially bigger problem.
Taking a step back, and asking “Why adjust ore blocks for blast movement?” yields the answer “To maximize value that can be obtained post-blast.” If that’s the purpose, then the solution must start at the grade control model.
It should come as no surprise that, if the same grade control model is given to several ore control geologists at the same mine, different 2D ore blocks will be created. This is because humans are not capable of optimizing complex shapes for maximum value, even in 2D (Issaks, Treloar, and Elenbaas, 2014). But, if we use a 2D optimizer to create in-situ blocks, we ignore the distortion in the shape caused by blasting, the change in the swell at the flitch interface, and the dig direction/face angle. So, we have to construct a post-blast grade control model, and optimize ore blocks in that model.
Building a post-blast grade control model is difficult due to differential movement, swell, and an irregular shape (Figure 4). How can a model that starts with 1,000 blocks be turned into a model with 1,500 blocks, and still maintain a metal balance? Several innovative methods were developed specifically to address this problem.
Once the post-blast grade control model is created, with user inputs of minimum polygon size, cutoff grade(s) for primary and secondary classifications (if applicable), and flitch boundaries, optimal polygons are created on each flitch that maximize value and minimize dilution (Figure 5). The grade, tonnes (with calculated swelled density), and any geo-chemistry that existed in the model are given for each polygon that allow comparison between different iterations, imported or drawn polygons, and optimised polygons.
Tools were created to allow the mine to see real-time changes in ore block shapes (Figure 6). This enables geologists to use their operational knowledge to confidently edit ore blocks. Additionally, mobile applications have been created to allow field personnel to visualize the blast in virtual reality while in the field, to aid in visual ore-spotting, and to prevent confusion with field staking (Figure 7).
The limitations to this approach are similar to the limitations with the 2D translation method. Since blast movement is inherently variable, movement data in each blast is integral to accuracy of the postblast model and ore blocks. As a general rule, the more movement vectors that are created, the more accurate the optimized ore blocks. Of course, the insitu grade control model is the basis for ore block creation, so this must also be as accurate as possible.
The inputs to software operation are:
- Grade Control Model
- Blast Hole Layout
- Movement Vectors
- Post-Blast Topographic Survey
Comparing 2D Method to 3D Method
The following blasts demonstrate the differences between moving pre-blast ore blocks in 2D and optimizing ore blocks in 3D post-blast. These blasts were all monitored for movement, and all performed at the mine discussed in Product Development. Each analysis assumes the following:
- Mining face angle = 90 degrees (short flitches are mined)
- Minimum polygon size = 5 x 5 m
- Gold price = $1300/oz
- Each ore block polygon is analyzed in the post-blast block model
- Cutoff grade = 0.65 g/t
This blast was fired to the Northeast into a choked face (Figure 8). This blast occurred in a five-meter bench. The mine’s 2D ore blocks that had been moved horizontally with blast movement were compared with the optimized post-blast ore blocks. They are shown in Figure 9.
As Table 1 shows, an additional $172,000 is gained by mining optimized ore blocks. In this case, it is profitable to take some additional dilution in order to gain several thousand tonnes of ore.
This blast was fired to the Northeast into a choked face (Figure 10). This blast occurred in a ten-meter bench. The mine’s 2D ore blocks that had been moved horizontally with blast movement were compared with the optimized ore blocks. They are shown in Figure 11.
As Table 2 shows, an additional $198,000 is gained by mining optimized ore blocks. In this case, ore loss and dilution are both reduced.
This blast was fired to the Northeast into a choked face (Figure 12). This blast occurred in a five-meter bench. The blast timing included a tight “V” configuration near the initiation point, causing significant differential movement at depth.
The mine’s 2D ore blocks that had been moved horizontally with blast movement were compared with the optimized ore blocks. They are shown in Figure 13.
As Table 3 shows, an additional $246,000 is gained by mining optimized ore blocks. In this case, ore loss and dilution are both reduced.
Other Opportunities Uncovered
Since the creation of this software solution, it has been used at dozens of mines around the world, which has led to innovation not-possible before its existence. For example, with the knowledge of what is optimal post-blast, mines can now run an analysis of optimized in-situ ore blocks, compare them to the optimized post-blast ore blocks, and derive the value lost due to the blasting method and direction.
Simulations can be run to determine the net effect of different blast designs on the ore control outcome (financial implications to blast design). With this knowledge, questions such as “what effect would an increase (or decrease) in powder factor have on ore control?”, or “What is the financial impact to blasting toward the South instead of the West?”
The results can enable short-term planning optimization, as well as dilution forecasting for feasibility studies.
Applying a 2D movement solution to a 3D movement problem ignores important considerations that are unavoidable: differential movement, heave, flitch interface changes, and dig direction. Applying a 3D optimizer to a post-blast grade control model provides a tool for optimizing value, providing situational awareness, and enabling ore control geologists to visualize post-blast ore locations. In the three blasts shown here, over $600,000 in additional value is gained by use of the post-blast optimizer.
Sometimes, it makes fiscal sense to take dilution in order to get a higher after-cost value in ore. In other cases, it makes more sense to throw some ore away that would cause too much dilution. Regardless of the case, using a sophisticated mathematical algorithm to find the best-possible ore block shapes leads to the highest value possible. Using an optimizer pre-blast is adequate for planning, but taking optimized 2D shapes and moving them horizontally for blast movement leaves much potential benefit behind.
The ore control ore/waste delineation process should only be conducted post-blast, as the distortion, heave, and post-blast shape of the muck dictate what the optimal solution should be.
The knowledge gained from this new ore control method also allows for short-term planning optimization, blast direction forecasting, and calculation of the financial impacts to blasting on ore control.
- Hunt, W. (2015). Increasing Recovered Grade in a Narrow Vein Surface Deposit through Blast Movement Monitoring. Africa/Australia. Adelaide: AUSIMM.
- Hunt, W and Thornton, D. (2013). Modelling vs. Monitoring: The Cost of Variation. SME Annual Conference. Denver: SME.
- Isaaks, Barr, and Handayani. (2014). Modeling Blast Movement for Grade Control. 9th International Mining Geology Conference (pp. 433-439). AUSIMM.
- Issaks, Treloar, and Elenbaas. (2014). Optimum Dig Lines for Open Pit Grade Control. 9th International Mining Geology Conference (pp. 425-432). AUSIMM.
- La Rosa, D. a. (2011). Blast Movement Modelling and Measurement. APCOM. Wollongong.
- Loeb, J. a. (2014). A Cost Benefi t Analysis to Explore the Optimal Number of Blast Movement Monitor Locations. Mining Geology Conference. AUSIMM.
- M Fitzgerald, S. Y. (2011). Blast Monitoring and Blast Translation – Case Study of a Grade Improvement Project at the Fimiston Pit, Kalgoorlie, Western Australia. Eigth International Mining Geology Conference. Queenstown, NZ.
- Thornton, DM. (2009). The Application of Electronic Monitors to Understand Blast Movement Dynamics and Improve Blast Designs. 9th International Symposium on Rock Fragmentation by Blasting – Fragblast 9.