Practical and accurate modelling of SMUs for optimisation of narrow vein open pit deposits

A bit of background…

When running the pit optimisation or Life of Mine (LOM) schedule, it is always advisable to ensure that the block model cells are representative of the actual degree of selectivity (SMUs) that can be achieved during practical mining operations. The SMU (selective mining unit) refers to the smallest regular cell size that can be mined selectively.?

Time and again, the mining industry is challenged with the task of evaluating orebodies that are narrow, with varying widths and splitting into lenses. Due to their nature, these narrow-vein deposits cannot be economically extracted at their insitu orebody thickness without inevitably mining significant non-economic (waste) material to a certain extent. The waste material that is not separated from the narrow-vein ore during stages of mining leads to a decrease in mean ore grade relative to the original expectation. This concept, known as ore dilution, is illustrated briefly on Figure 1. The amount of ore dilution is dependent on the selective mining unit (SMU).?

Figure 1: Internal vs external dilution in a mining block

As you can imagine, narrow-vein deposits are characterised by high dilution and as such the economic value of ore in narrow-vein deposits is highly influenced by dilution. Therefore, it is essential to ensure that the dilution factor in narrow-vein deposits is modelled accurately. As I have stated before, one of the main consequences of dilution is the increase in ore tonnage sent to the processing plant with a reduction of mill feed grade. Lower feed grade translates to lower mill recovery, and this accompanied by increasing processing costs translates to less income. This has an impact on the size of the optimised LG phases that are generated.

Open pit optimisation solutions such as Datamine’s Studio NPVS use the block model information and the defined economic parameters to calculate the costs of extracting certain blocks and the value realised from extracting those particular blocks. The software determines the block values and takes into consideration the geotechnical and physical constraints in order to determine a set of LG phases that maximises a specific user defined goal, usually the Net Present Value.

Previously, on the article titled “The 4Ws of dilution in open pit planning”, I discussed the some of the block model dilution techniques that are available for generating practical SMUs and clearly highlighted that modelling of dilution is not a one-glove-fits-all phenomenon: The answer to the question “which dilution modelling technique to use” is dependent on the type of deposit in question as well as the operational aspects. With that in mind, it is important to think carefully about the approach you are going to use to model dilution on your block model.

To jog your memory a bit, that article briefly discussed the following techniques:?

? Using a flat, mathematical dilution value (percentage) that is deemed appropriate for the deposit (based on historical information, rules of thumb or even a thumb-sucked value) and applying that value globally on the block model;

? Using the REBLOCK process in Datamine Studio to create block model cells matching the SMUs and re-calculating the block model attributes (e.g. grades, contaminants etc.) according to user-defined criteria. During this process, some ore will be diluted with waste material;

? Using the DILUTMOD process in Datamine Studio to dilute grades in a parent cell by a user-defined dilution width (skin-dilution). The block model grades are adjusted in parent cells which have adjacent cells of a different rock type; and

? Using Mineable Shapes Optimiser (MSO) tool to model dilution for narrow-vein deposits:

For more information of the different dilution techniques, see the article titled “The 4Ws of Dilution in open pit planning”

Due to the manner of application, dilution factors that are applied mathematically or using the reblocking method can overstate or understate the value of narrow-vein deposits, resulting in incorrect optimisation results.?This is illustrated on Figure 2 below.

Figure 2: Comparison between the different applications of dilution in narrow deposits using different dilution techniques

Using Mineable Shapes Optimiser (MSO) tool to model dilution in narrow-vein deposits…

In this section of the article, I am going to elaborate a little bit more on the alternative, pre-processing method to model dilution for narrow-vein deposits using the underground stope optimisation tool, Mineable Shape Optimiser (MSO). MSO is a plugin to a range of datamine software that is conventionally used for economic evaluation and generation of optimised underground stopes.?

By considering an SMU as a stope, the MSO tool is then used to produce optimised 3D stope-shapes that maximises the recovered resource value above a cut-off grade/value. MSO’s optimised 3D stope-shapes take into consideration the orebody geometry and caters for practical mining parameters (such as minimum and maximum mining width, anticipated footwall and hangingwall dilutions, minimum and maximum stope height and wall angles).

MSO will output SMU sized wireframes that have been thoroughly evaluated to ensure that they meet the proposed cut-off (after hangingwall and footwall dilution) and minimum mining width that matches the SMU size. These wireframes can then be used to re-build the block model (using the MODSPLIT function on any of the Datamine studio products) to match the optimal shape of each wireframe. Prior to optimisation, the material inside these MSO wireframes can then be flagged as ore while the material outside the wireframes can be flagged as waste as shown on Figure 3 below. The portions of the block model that are flagged as ore using the 3D MSO wireframes will include the internal footwall and hangingwall dilution that is required to establish those particular SMUs. All the material within the optimised SMU is assumed to be achievable for extraction. At the end of this process, you will have a block model that has pre-defined classification of ore and waste that speaks to your SMU definition and defined cut-off grade.?

Figure 3: Comparison between the original block model and the block model that has been flagged using the optimised MSO stope-shapes.

After pre-processing the block model using MSO, you can then specify the material categories on Datamine’s Studio NPVS according to the outputs of MSO rather than letting Datamine’s Studio NPVS calculate and determine what is ore/waste. This process is shown on Figure 4 below. The material that has been mapped as ore during the block model import into Datamine’s Studio NPVS must be processed and will incur the associated processing cost.?

Figure 4: Options available with regards to how ore (and conversely, waste) is identified and reported on Datamine’s Studio NPVS.

In conclusion…

By applying this method, the mine planning engineer will be in a position to make better and informed decisions with respect to the final pit limits for narrow-vein deposits. The reason being that the minimum mining unit (SMU) and ore dilution will be considered from a perspective that is more aligned with what can actually be executed at the mine. As the dilution within each SMU is modelled accurately, the processing plant can be modify accordingly to accommodate the diluted material downstream.