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.

Heap Leach Application provides Metallurgists and Operators with an up-close insight into the crushing process by offering the capability of predicting the particle size distribution in real-time and material influence on the final moisture content and reclaim difficulty of heap leach tailings.

Challenge 1

High fines material (-100 mesh) reporting to the heap leach stacks, affecting leaching efficiency, leach pad permeability, and promoting high moisture conditions.

Solution

Particle Size Prediction Model predicts real-time particle size distribution of crusher product and monitoring crusher performance based on power draw. By using the -100 mesh virtual sensor to evaluate the impact in the leaching pads recovery and reclaim difficulty, in order to recommend adjustments to the crushing plant operation.

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.

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.

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:

Define strategies for the reclaim of heap leach tailings modules, and see If additional resources or equipment is required.
Identify high risk stacked modules in advance and enforce standard operating procedures(Assists in hazard identification and risk assessment).
A better understanding of the root causes to drive process improvement.