Case Study - Pattern Expansion

1 Introduction


The main objective was based on blast pattern expansion with automatic adjustment of the burden, spacing, stemming, sub-drilling and number of holes in order to guarantee the production demands in terms of blasted volume of the Tasiast mine, operated by the Kinross group.



2 Challenges and objectives


To solve this problem was necessary to create an optimized blast based in a mathematic model (where the fragmentation levels were maintained), associated with an estimated final cost. The results of this technique demonstrate the cost reduction on blasts while the fragmentation was guaranteed.



3 Solution Applied


Firstly, to start the optimization process it was necessary to have a real forecasting model before simulating any changes in the project parameters. For this reason it was essential to calibrate the Kuz-Ram rock factor. Table 1 shows the initial state of the rock calibration factor, predicted and actual fragmentation. Several blasts were analyzed to find the most accurate rock factor for this study.

Table 1 - Rock Factor Calibration Process (O-Pitblast system)



In order to obtain the ideal blasting design parameters, it was necessary to create a non-linear problem. In other words, define the dependent variables, empirical restrictions and fragmentation demands (in this case, 90% below 700 mm / 27.56 in). The pattern was expanded until the fragmentation limit was reached. In table 2 shows the evolution of each stage in terms of changes and results.

Table 2 - Pattern expansion evolutionary stages


Controllable changes were applied at any improvement stage and detailed fragmentation analysis were performed in order to control the blast results. The pattern was expanded until the fragmentation limit was reached.



4 Customers Benefits


With O-Pitblast’s blast optimization algorithm was possible to reduce $229,361 in cost, 605,307 m3/791,712 yd3 of rock (figure 1 and figure 2).


Figure 1 - Drilled Holes.

Figure 2 - Drill and Blast Savings.


The optimization process, operation and field practices demonstrated that a careful analysis must be done in order to match the mathematical optimization and nature behavior to obtain the best and desired results.



Authors:

Vinicius Miranda | Francisco Leite | Gean Frank

O-Pitblast

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