To create profitable operations, companies must make substantial upfront investments in drilling, blasting, transporting, and processing ore. One of these critical but often underestimated factors is stockpile variability.
Although designed to serve as buffers, stockpiles can also conceal inefficiencies. Variations in ore grade, composition, humidity, and oxidation can all affect the performance of the processing plant. When variability is unmonitored, the cost of processing increases along with the difficulty in meeting production goals.
Maintaining consistent recovery and optimizing stockpile variability has become essential with increased digital transformation and mine-to-mill optimization efforts.
There is a common misconception that the outside of a stockpile is representative of its ore in a similar manner to a box of chocolates. The reality is that stockpiles often exhibit a tremendous amount of internal variability.
These materials can differ in:
Variability within a stockpile ultimately leads to uncontrollable variation in grinding, flotation, and leaching operations.
To optimize the processing of ore and ultimately the recovery of the desired end minerals, modern processing plants are designed to operate with fixed and consistent characteristics of the input material.
If the quality of the material being processed varies, it leads to several operational difficulties, including the instability of the processing plant.
Operators may be forced to deal with changing conditions that make it almost impossible to maintain steady production.
Ore property changes can reduce the performance of reagents and the liberation of minerals, which can decrease the recovery of metals.
Processing harder ore may take more time to grind to the required size, significantly increasing costs and reducing production rates.
Variability in the characteristics of the material processed may cause additional stress on mills, pumps, crushers, and other processing equipment.
Unreliable composition of stockpiles makes it impossible to accurately predict production and achieve goals.
Ore bodies are not homogenous and may contain areas of varying mineral and rock composition.
Different areas of the same mining pit may also be characterized by different rock and mineral compositions.
Some mining operations rely on stockpiles to blend ore naturally.
If no formal blending strategy is implemented, different ore can become segregated in stockpiles.
There is often a segregation of ore by particle size when stockpiles are formed.
Storage of ore that becomes segregated by particle size can negatively impact ore quality when it is reclaimed from a stockpile.
Exposure to the environment can change the properties of the ore.
Oxidation of sulfide ores can change the surface of minerals and make it more difficult to float. This increases the demand for reagents and reduces the recovery of minerals.
Weather changes through different times of the year can change humidity and moisture levels. Rain and changing levels of humidity can make the materials in a stockpile too moist.
Excess moisture can make material handling, screening, and processing more difficult.
Variability of stockpiled material can especially affect grinding circuits and has the following impacts when harder ore is fed into the mill:
Grinding is typically the largest energy cost for mining, and harder ore can make profitability more difficult to achieve.
Reagent consumption, attachment of bubbles to particles, and the grade and recovery of the concentrate all depend on the mineralogy and oxidation state of the stockpiled material.
If there is too much variability in the stockpiled material, flotation will not perform optimally.
Variability in the stockpiled ore can affect different leaching operations by changing the leaching kinetics and the recovery and consumption of the leaching reagent.
Leaching conditions do not impact all stockpiled ore the same.
Varied recoveries across multiple processing stages lead to significant recovery losses.
Given the size of most mining operations, a small impact on recovery can lead to millions of lost revenue.
Variability in stockpiles creates challenges that are beyond metallurgy and actually impact business metrics. Some of the challenges that stockpile variability creates include:
In an already challenging mining environment, these impacts can determine the success or failure of an operation.
Streamlining blending helps ensure a consistent feed stream.
Mining companies may:
Blending can cut down on variability that would occur prior to ore entering the processing plant.
Modern tracking provides insight into what stockpiles are comprised of.
Forms of technology can be:
Tracking systems may give operators an insight into what material is entering the plant.
Digital twin technology can be used to make a virtual model of a mining operation and its processing.
Use of digital twins enables operators to:
This technology has been adopted by many of the major players in the mining space to enhance the performance of their plants and help reduce variability losses.
Implementing AI and predictive analytics modules has a positive impact on:
This enables a more proactive approach to stockpile management and performance rather than a reactive one.
Guarantees consistency among stockpiles of raw materials and the practices used to recover them.
Recommended techniques are:
Stockpile blending can also be improved by reducing segregation.
The integration of mining activities with processing operations is referred to as "mine-to-mill."
The mine-to-mill framework focuses on the entire value chain rather than treating mining and processing as separate activities.
By combining data from geology, mining, and processing, stockpile and plant variability is more easily managed.
Digital technology is actively being integrated into mining operations to address variability. Some of the trends driving this change are:
All of these will provide mining operations with greater control of both the ore being processed and the efficiency of their systems.
Stockpile variability is one of the main unaddressed causes of recovery loss for mining companies. Variability of the ore being mined on such characteristics as grade, hardness, moisture, oxidation, and mineralogy of the ore can lead to costly problems at the processing plant.
Failure to address variability will lead to reduced recovery, increased operating costs, and reduced profitability of the mine.
Structured blending, real-time control, digital twins, and AI control and optimization will help mining companies reduce variability and improve the operations of their processing plants.
As the mining industry embraces digital technologies, variable stockpile management will help improve recovery rates and the seamless operation of the mine for years to come.
Variability in mining stockpiles is the difference in ore such as grade, hardness, mineralogy, moisture, and oxidation.
Variability impacts recovery by reducing the efficiency of grinding, disrupting flotation, increasing energy costs, and reducing the overall metal recovery.
The blending of ore is an attempt to make the feed to the processing plant more uniform, which helps stabilize the processing, improve recovery, and increase throughput.
AI helps take the guesswork out of managing variable stockpiles by predicting feed quality. It can also help with blending strategies and recovery performance and making real time decisions.
Mine-to-mill optimization focuses on integrating and optimizing mining and processing operations, which ultimately enhances throughput, recovery, and overall performance of the operations.
ReferencesMDPI. (2023). Effect of Long-Term Stockpiling on Oxidation and Flotation Response of Low-Grade Sulfide Copper Ore. Retrieved from https://www.mdpi.com/2075-163X/13/2/269
ScienceDirect. (2023). A Laboratory-Scale Characterisation Test for Quantifying the Size Distribution of Blended Ore. Retrieved from https://www.sciencedirect.com/science/article/abs/pii/S089268752200440X
LinkedIn. (2023). The Strategic Role of Blending in the Mining Industry. Retrieved from https://www.linkedin.com/pulse/strategic-role-blending-mining-industry-comprehensive-jivtode-al0wc
Metso. (2024). Optimizing from Mine to Mill to Mine with Digital Twin Technology at Newmont Lihir Gold Mine. Retrieved from https://www.metso.com/insights/case-studies/...
MDPI. (2021). Digital Twins with Distributed Particle Simulation for Mine-to-Mill Material Tracking. Retrieved from https://www.mdpi.com/2075-163X/11/5/524