Strategic Mine Planning for Open Pit Mines – The Integrated Way!!! (Part 2)

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Part 1 of this article was published a couple of weeks ago and it discussed briefly the first three steps involved in the strategic planning process. These involved firstly the determination of the key objectives, constraints and KPIs which drives and supports the organisational future outlook. Thereafter, the article discussed the pit optimisation process, which is all about determining the optimal outline or shell to be mined in order to maximise the profit while satisfying operational requirements. Lastly, Part 1 briefly discussed the process of converting the selected mathematical pit shell into a detailed, operative pit design. This is where Part 1 of this article stopped. Furthermore, the article titled “There is no way out of a bad design” was published a week ago and it covered in detail some of the key aspects to consider when designing an operative pit. Part 2 of the series is a continuation from Part 1, and it will be focussing on Pushback Optimisation and Selection as well as Strategic Scheduling.

4. Pushback Optimisation and Selection

Once the final, operative pit design is completed on Datamine’s Studio OP, the next step in the strategic planning process involves importing the final, operative pit design back into Datamine's Studio NPVS in order to continue with the Pushback Optimisation and Analysis process. The fundamental objective of the Pushback Optimisation and Analysis phase is to create pushback shapes which attempt to meet defined primary targets, such as ore tonnage, stripping ratios or mined grade

As discussed previously on this article titled “What are pushbacks and why do we really need them in open pit mines”, pushbacks can be defined as a series of manageable phases that can be exploited with the available mining equipment and that can meet practical geotechnical and operational mining constraints. Pushbacks play a very crucial role in open pit mine design and optimisation. Production scheduling is always based on the underlying set of pushbacks, so the manner and approach in which pushbacks are selected, designed, and scheduled has a remarkable impact mainly on the mine’s operability and profitability. Pushbacks that are well defined can result in an increase in NPV while minimising the pit strip ratio. This is obviously dependent on the characteristics of the orebody and dimensions of the final pit (Araya et al.,2020).

Best case NPV vs Worst Case NPV

For any deposit under specific economic, production and engineering parameters and/or assumptions, pit optimisation solutions such as Datamine’s Studio NPVS will output the “Best Case NPV” and the “Worst Case NPV” report.

The worst case NPV occurs when the final pit is mined bench-by-bench, from top to bottom, mining each bench completely before moving to the next bench as shown in Figure 1. This results in the lowest Net Present Value (NPV) for the pit, and this is also a scenario with the biggest financial risk.?

Figure 1: A final pit with no psuhbacks, producing the "Worst Case NPV"

The best case NPV occurs when each of the nested pit shells are mined one after the other, maximising NPV by mining ore as early as possible while deferring waste mining as shown in Figure 2. A good set of pushbacks helps increase the NPV while you manage the financial risks that are associated with external global factors – well at least better than a pit with poorly defined or no pushbacks.

Figure 2: Final pit mined using pushbacks, producing the "Best Case NPV"

The difference between the NPV generate by the "best case" and the "worst case" shown in Figure 3 determines the feasibility of, or an opportunity to add a pushback or two to the pit optimisation process. In this case, beyond the pitshell with a Profit Factor 0.24, the implementation of a pushback should be investigated. If the NPV generated by the best case and the worst case differ slightly, the implementation of a pushback will not add value to the project. The mining sequence for that particular pit is unimportant from an economic point of view.

Figure 3: "Best Case NPV" vs. "Worst Case NPV"

Remember that a pushback is nothing but a series of nested pits that conform to not only the minimum pushback width, but helps the schedule to provides the highest dollar value. As discussed previously, pushbacks need to be able to address the following critical points:

• Geometric and size constraints - which includes the pushback width, smoothness and continuity (Bai et al.,2018);
• Geotechnical variables - to ensure that pushbacks always satisfy the geotechnical slope considerations; and
• Quality constraints - which includes things such as the desired amount of material (ore and/ore waste) as well as the overall strip ratio.

Typical traditional approach for pushback selection involves selecting specific pit shells as potential pushbacks from the ultimate pit graph shown in Figure 4 below. The pushbacks are selected from the nested pit shells taking into consideration the practical mining constraints such as minimum bench width.

Figure 4: Nested LG Phases marked with remarkable pit shells that can be selected as potential pushbacks.

For example, if you look at Figure 4, one may have selected the pit shell at Profit Factor 0.12 as a possible pushback (provided it meets the minimum width requirements). This is because that is the last significant pit shell before a significant jump in the strip ratio shown on the next pit shell with a Profit Factor of 0.14. The same applies to pit shells with Profit Factor 0.22, 0.28 etc. As you can see, these pushbacks are selected from the nested pit shells manually, based on the experience of the engineer and various empirical rules, which may lead to suboptimal results.

Luckily, Datamine’s Studio NPVS has the tools to generate optimal pushbacks which takes into consideration the primary ore targets, minimum mining width as well as location and depth constraints. In this example two pushback scenarios were considered.?The difference between the two scenarios is based on the minimum pushback width, pushback quality constraints as well as depth constraints:

Scenario 1

The premise for scenario 1 is based on the generation of 3 pushbacks using the following constraints:

• Minimum pushback width – 100m
• Ore Tonnage per pushback – 4.5Mt
• Depth constraints

- Pushback 1 up to elevation 1262

- Pushback 2 up to elevation 1172

- Pushback 3 up to elevation 1052

The output of the pushback optimisation for this scenario is represented in Figure 5. It is visible from the graph that significant waste has been deferred into Pushback 2 and 3 in order to get to the ore in Pushback 1 faster and maximise the NPV.

Figure 5: Pushback optimisation for Scenario 1.

The NPV for this Scenario is around $547.13 million. Once the pushbacks are generated, then you have to schedule the optimised pushbacks in order to verify if production targets can be met with the available or proposed equipment requirements as shown in Figure 6. This is an iterative process.

Figure 6: LOM schedule based on the Scenario 1 optimised pushbacks.

At this stage of the analysis, it is possible to consider the capital cost of establishing each pushback into the schedule. This is vital for the analysis. If you are struggling to meet the scheduling targets, it means the underlying pushbacks are not good enough and they have to be re-adjusted and re-optimised. The relative size of the 3 pushbacks on plan view is shown on Figure 7.

Figure 7: Relative sizes of the Scenario 1 pushbacks.

Scenario 2

The premise for scenario 1 is based on the generation of 5 pushbacks using the following constraints:

• Minimum pushback width – 50m
• Ore Tonnage per pushback – 3.0Mt
• Depth constraints

- Pushback 1 up to elevation 1262

- Pushback 2 up to elevation 1222

- Pushback 3 up to elevation 1172

- Pushback 4 up to elevation 1112

- Pushback 5 up to elevation 1052

The output of the pushback optimisation for this scenario is represented in Figure 8. It is still visible that waste has been deferred into subsequent pushbacks order to get to the ore in Pushback 1 faster and maximise the NPV.

Figure 8: Pushback optimisation for Scenario 2.

The NPV for this scenario is around $561.58 million. As I have discussed, pushbacks with narrower minimum pushback width have a relatively higher NPVS and provides for some flexibility in terms of scheduling. Numerous available pushbacks give you scheduling flexibility since you have many areas to mine from, thereby catering for a highly selective mining in terms of targets (Bai et al., 2018). However, such pushbacks have low productivity (little flexibility) and relatively higher operating costs.?Again, one should schedule the optimised pushbacks in order to verify if production targets can be met with the available or proposed equipment requirements. The schedule based on the 5 pushbacks is shown on Figure 9.

Figure 9: LOM schedule based on the Scenario 2 optimised pushbacks.

This was a simple schedule based on the total rock tonnes per semester as well as the total ROM per semester. More targets such as strip ratio etc. can be used. The relative size of the 5 pushbacks on plan view is shown on Figure 10.

Figure 10: Relative sizes of the Scenario 2 pushbacks.

It is very easy and straightforward to optimised pushbacks using Datamine’s Studio NPVS. This powerful tool has the capability to also use pushback adjustment boundaries in order to influence the location and size of a pushback in 3D.?It is also quick to run pushback scenarios to ensure that you can produce a good set of pushbacks with minimal effort.

Once you have selected the pushbacks that you are confident about, that can meet all your scheduling targets, the next step involves converting the 3D mathematical pushback shells into detailed, operative pushback designs with access ramps, benches, and berms. Every pushback should have access to it. If pushbacks share the same final wall, the preference is to generally use the long-life haul roads servicing both pushbacks rather than short-life roads per pushback as long-life haul roads reduces overall road construction costs and operating costs

5. Strategic scheduling

Once the operative designs for all the pushbacks are done, you need to import them back into Datamine’s Studio NPVS to finalise the strategic schedule. Figure 11 shows the mathematical pushback shells from Datamine’s Studio NPVS vs. the designed, operative pushback designs from Datamine’s Studio OP.

Figure 11: Comparison between the mathematical pushback shells and the detailed, operative pushback designs.

The objective of strategic scheduling is to determine the extraction sequence of ore and waste blocks in order to maximize the NPV of the mining operation within the existing economic, technical, and environmental constraints (Gholamnejad et al., 2020). In general, a mine production schedule seeks to answer the following questions (Van Dunem, 2016):

• Whether a given block in the model should be mined or not;
• If it is to be mined, when should it be mined; and
• Once it is mined, where should it be sent.

At this stage, one is still optimising the material movement out of the pit and not necessarily the material feed into the plant. Optimising the plant feed requires one to run the material allocation optimiser, which helps the engineer determine how best to transport, stockpile, and treat material out of the pit in order to generate all required products. This will be discussed briefly on Part 3 of the article coming soon!

Once the designs are there, one can define a haulage network and also run a high-level haulage analysis and optimisation. Haulage optimisation is all about answering 2 keys questions:

• How many truck hours are needed per scheduling period in order to move X amount of tonnes; and
• How many tonnes can be moved per scheduling period with N amount of truck hours.

Figure 12 shows a Life of Mine (LOM) schedule that seeks to determine the amount of truck hours that are required to produce 7.5Mt per semester for the LOM. It is visible that as the schedule progresses, more truck hours are required to maintain the same production as hauling distances and cycle times increase with depth. The NPV for this scenario is around $622.1 Million (not considering the capital that may be required to increase the truck hours).

Figure 12: Total production vs the required truck hours per semester

Figure 13 show the LOM schedule the seeks to determine the total tonnes that can be potentially mined with the available truck hours per semester. The total tonnes produced decreases as the schedule progresses because the truck hours are not sufficient to sustain the production levels at 7.5Mt per semester. The NPV for this scenario is around $608.2 Million, which is less than what we have in the previous scenario (due to a much longer sceduling timeframe, which has an impact on the time value of money aka NPV).

Figure 13: Available truck hours vs total possible production.

This can be further analysed using the capital expenditure evaluation functionality on Datamine’s Minemax Scheduler. The extremely powerful?Capital Expenditure?utility on?Datamine’s Minemax Scheduler?can be used to determine if it is worthwhile for your mine to incur a certain Capital Expenditure in order to maximize the Net Present Value (NPV) of the operation. In simple terms, should you spend X amount of money to gain Y units of capacity and if yes, when should you spend that money in order to maximise NPV.

LOM scheduling is a fun and iterative process when the pushbacks are not only well defined, but well designed as well. Once the final strategic schedule has been defined, the scheduling information (destination, sequence number etc.) can be sent to Datamine Studio OP through a block model output shown on Figure 14. This scheduling information can be used on Datamine Studio OP to guide the solid-based LOM schedule.

Figure 14: Example of information about Rock Type and Processing Destination stored on a block model output from Datamine's Studio NPVS.

Part 3 of the article will be focussed on optimising the material allocation i.e. optimisation of the destination schedule (plant feed schedule as well as the dump schedule). It will also focus briefly on the Economic Evaluation and Analysis of the strategic plan.

References:

1. Araya, A., Nehring, M., Vega, E. & Mirnda,N. 2020. The impact of equipment productivity and pushback width on the mine planning process.
2. Bai, X., Marcotte, D., Gamache, M., Gregory, D. & Lapworth, A. 2018. Automatic generation of feasible mining pushbacks for open pit strategic planning.
3. Gholamnejad, J., Lotfian, R. & Kasmaeeyazdi,S. 2020. A practical, long-term production scheduling model in open pit mines using integer linear programming.
4. Van Dunem, A. 2016. Open-pit mine production scheduling under grade uncertainty.