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Leaching Optimization
Introduction
How do I use the Heap leach application predictions?
How do I use the Heap leach application predictions?

Describes how a user(Operator/Metallurgist) can use the models to make informed decisions in the Plant

Mark de Geus avatar
Written by Mark de Geus
Updated yesterday

The crushing process in mineral processing has an inevitable influence on downstream processes that depend on the Particle Size Distribution of the material. Processes such as heap leaching can be performed more efficiently if the liberation of the mineral is to the required extent. Also, the material property influence of the ore has a considerable effect on the final product or waste material produced. The material properties of the heap leach tailings will affect the efficiency of reclaiming this material, and so it is essential to monitor any variations and project their impact.

Challenge 1

High fines material (-100 mesh) reporting to the heap leach pad or modules, affecting leaching efficiency, and causing high acid consumption due to localized flooding.

Our Solution

Particle Size Prediction Model predicts the real-time fine fraction of crusher product and monitoring crusher performance based on power draw.

Value

The model can be applied to:

  • Adapting plant feed in real-time to prevent undesirable effects or fines contamination that will affect downstream processes.

  • Avoiding undesirable performance by quickly responding

  • Effective production planning and resource allocation

  • Manage pad irrigation times based on material properties to avoid unnecessary acid consumption.

Challenge 2

Our ability to reclaim heap leach tailings effectively is being limited by high moisture content and possibly other material properties.

Our Solution

  • See reclaim difficulty predictions and final moisture content of different modules and recommended reclaim throughput(Reclaim rate Model) from a stacked module predictive report generated by the application.

  • Property influence contribution to reclaiming difficulty by using a material influence model coupled with the reclaim model.

Value

The model can be applied to:

  • Prevention of equipment stoppages (downtime)

  • Maximizes equipment utilization and recommendations for a suitable reclaim rate that avoids stoppages.

  • Effective material handling planning

Challenge 3

The high final moisture content in gravel is affecting heap stability, stacking angles, stacking height, and increases hazards.

Our Solution

Material influence model or contribution to predicting instability of stacked material based on moisture content and other properties.

Value

The model can be applied to:

  1. It helps to manage pad irrigation strategies, based on material properties.

  2. Potential improvement on hazard identification and risk assessment of leached gravel modules.

The dashboard below summarises the potential recommended actions that can be deduced from the graphs.

Steps:

  1. Select a historic mode in the time picker and chose period range for analysis

  2. Observe the Line graphs to ensure their within range or below the threshold

  3. See the material property influences showing their contribution to the prediction

  4. Recommended action to mitigate the deviation from desired range e.g. Adjusting crusher CSS, ore blending, reducing reclaimer speed(throughput), etc.

  5. Alert indicating a threshold breach that notifies the operator by changing color and a beeping sound.

For more info, please do reach out to us via Intercom. We'd be happy to help!

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