Webinar: Detailed Analysis of How to Anticipate the Impact on Pit Boundaries

Welcome to the K-MINE webinar on the Pit Optimizer module and its capabilities for sensitivity analysis of open pit boundaries.

The Pit Optimizer is a strategic planning tool for mining engineers. Its primary purpose is to help define business strategies that maximize cash flow and profitability. Before committing significant resources to a mine site, management needs to agree on strategic development benchmarks - and these should ideally be reviewed and updated every year.

At the end of this planning stage, a mining company should have clearly defined optimal pit limits, split by intervals and volumes.

Today's webinar focuses on the key factors that affect the parameters of the final pit boundary. We apply sensitivity analysis of pit walls to different variations of basic parameters and take a closer look at the economic consequences of changes in cutoff grade.

When searching for the final boundary, you need to constantly evaluate all elements of the block model. Sensitivity analysis allows you to select the main indicators and assign them different priorities that affect the cost of each block.

For example, if we set the durability of pumping equipment as one of the indicators, we can calculate the impact on mining costs per ton of rock mass. The impact will not be as substantial compared to the durability of the main equipment - which means we can adjust these indicators as needed.

The main question is: which parameter or parameters are most crucial for profitability, and is it worth allocating all the resources on site?

When creating final pit boundaries, the Pit Optimizer requires three types of source data:

The first is the block model with basic parameters - block coordinates, block sizes, density for each material type, material class attributes, and quality parameters of available components.

The second is economic parameters - mining costs, processing costs, transportation costs, and the final price of the product.

The third is mining conditions - pit wall slope angles, cutoff grade, and related constraints.

Accurate input data is essential for reliable planning results.

In this demo, we use a block model with a regular block size of 10x10x10 meters. The scenario uses different mining costs for various rock types and different transportation costs depending on elevation height.

Two ore types are defined: rock type 2 corresponds to high-grade ore, and rock type 4 corresponds to low-grade ore. The data is used to determine the net present value (NPV).

For this example, the open pit operates at a capacity of 1.85 million tons of ore per year. The expected price adjustment coefficient is set at 1.6, and the cutoff grade is set at 16% - consistent with the majority of Eastern European deposits.

To start, go to Planning and select Optimal Pit Boundaries. In the dialog box, create a new scenario, specify the name and project file path, and add the block model.

The software creates an additional file that adapts the selected block model for the Pit Optimizer module.

Price adjustment coefficients are set from 0.6 to 2.0 in increments of 0.2, with an additional factor of 3 to assess the open pit potential.

In the block model settings, select the rock type attribute and the specific weight field. Display values with one decimal sign at one million scale.

The number of benches from the slope parameter affects how the final boundary is constructed - the steeper the pit wall slope angle, the bigger this value should be. In this example, it is set to 3.

Three methods are available to set pit slope angles: by area, by vector, and by properties. All methods are based on the same approach, with the vector method being the most fundamental, as it allows different pit wall slope angles for different directions.

For this demo, we set angles by area. Four geotechnical areas are created using polylines and the Extrude Wireframe command. The slope angles are assigned as follows: Zone 1 at 45 degrees, Zone 2 at 40 degrees, Zone 3 at 35 degrees, and Zone 4 at 50 degrees. The default angle for uncovered areas is 42 degrees.

The final product cost is set at $100 per ton of concentrate.

Processing costs differ by ore type. Rock type 2 (high-grade ore) costs $15 per ton to process, while rock type 4 (low-grade ore) costs $18 per ton. Losses and dilution are set at 2%.

The yield calculation uses a theoretical formula with three indicators: reserve grade, recovery grade (65%), and tailings assay (10%).

For the cutoff grade, rock types 2 and 4 are processed when the magnetite indicator exceeds 16%. Rocks with magnetite content below 60% are treated as waste.

Mining costs are set by rock type: ore at $6 per ton, hard rock waste at $5 per ton, and soft waste at $4 per ton. Mining costs for ore are typically higher than for waste because they often include royalty tax and other production costs.

Transportation costs are set according to elevation height. Since the transport equipment is selected correctly and dump trucks are loaded nominally regardless of bulk density, transportation cost is the same for all rock types.

Blocks with material class attribute 100 serve as a constraint, preventing the open pit from extending beyond this boundary.

The K-MINE Pit Optimizer uses an upgraded Lerchs-Grossmann algorithm. The calculation completes within seconds.

At a price coefficient of 0.6, only 415 blocks are to be extracted - mining operations would not be profitable under these conditions. As the price adjustment increases, the number of mining blocks grows, and the volume of extracted rock mass expands.

The engineer must determine the optimal price adjustment factor for further activities - this is the most critical step, as it defines the open pit's scope and the company's opportunities. Typical indicators include profit, profit factor, profit margin, income, NPV, and quantity of mined ore.

For this example, the boundary with a coefficient of 1.6 is selected as the basic scenario for all future comparisons.

The first sensitivity scenario increases mining and transportation costs by 30%. For best practice, analysis should be performed with smaller steps (e.g., five times with a 5% step), but a 30% step is used here for demonstration purposes.

The results show that a 30% cost increase significantly decreases the number of mining blocks across all coefficients.

A second scenario with a 30% cost reduction shows the open pit with the 1.6 price factor expanding significantly. This indicates it is reasonable to explore opportunities to reduce mining and transportation costs to increase the mine's profitable life.

Comparing all three scenarios, the scope of the open pit depends significantly on these cost parameters. The key insight is that a slight increase in mining costs does not significantly affect pit boundaries, but a cost reduction in the opposite direction has a significant positive impact on production value.

The next scenario examines processing cost sensitivity. It is important to change only one parameter at a time - if multiple parameters change simultaneously, it becomes difficult to identify which factor affected the result.

With a 30% increase, high-grade ore processing rises to $19.2 per ton and low-grade ore to $23.4 per ton. The results demonstrate that the open pit boundary at the 1.6 price factor is not significantly different from the basic scenario.

Similarly, reducing processing costs to $10.5 and $12.6 per ton respectively shows a visible but relatively insignificant difference. The lower the processing cost, the more profit the company obtains, but the influence on final pit boundaries is minor.

This scenario examines how the cost of the final product affects the size of the resulting open pit. All other parameters remain the same as in the basic scenario.

Increasing the final product cost to $130 per ton results in a dramatic expansion of the open pit, practically reaching the restricting blocks.

Reducing the final product cost to $70 per ton shows the opposite effect. The final product cost affects the parameters of the extracted open pit more than any other factor analyzed.

Comparing all three scenarios, the difference in pit boundaries is substantial - more so than any other parameter group tested.

The final parameter tested is the cutoff grade, with scenarios at 12% and 20%.

At a 12% cutoff grade, the open pit expands widely - more low-grade ore is now included. However, this ore may require specialized processing technologies, result in higher processing costs, or affect product quality.

At a 20% cutoff grade, the open pit size radically decreases. When the boundary separating ore from waste increases, the pit shrinks.

The section view shows that the west pit wall is quite different from previous scenarios - confirming that cutoff grade is a highly sensitive parameter.

All results can be exported from the Optimal Pit Boundaries menu for detailed analysis - weights, volumes, rock types, and economic parameters such as income, profit, and expenditures are available, including export to Excel.

To select the optimal boundary, start by eliminating scenarios with minimum rock mass excavation, as mining companies aim to extend deposit life without losing profit. Scenarios with reduced final product cost, increased mining cost, and increased cutoff grade are eliminated first. Processing cost scenarios are also set aside due to minimal impact.

Three scenarios remain: increased final product cost, reduced mining costs, and reduced cutoff grade. Looking at the economics, income and NPV are significantly higher for the increased final product cost scenario.

Examining peak values for mining total rock mass, the increased mining cost scenario yields 1,559.2 million tons, while the reduced final product cost scenario yields just 36.5 million tons. This confirms that the final product price affects the final pit boundaries the most.

While the company has no control over the product price on the global market, this analysis helps understand the potential of the open pit - whether there is an opportunity to increase the volume of waste or whether it makes sense to hold back.

The sensitivity chart allows for preliminary predictions on transitional values, helping answer questions such as: what should the mining cost be to achieve a profit level of $800 million, or what profit would result from a 15% reduction in processing cost?

More scenarios lead to more accurate results. If you perform strategic planning, sensitivity analysis helps make the most reliable estimation - especially when answering the question of whether to mine, when to mine, and for how long.

The K-MINE Pit Optimizer uses an upgraded Lerchs-Grossmann algorithm for optimization.

The price adjustment coefficient is a factor that is multiplied by the price of the final product. In this demo, it was set from 0.6 to 2.0 in increments of 0.2, with an additional value of 3 to assess the full open pit potential.