Predictive Leach Analytics for Dynamic Copper Recovery Control
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Solution Overview
Problem
Existing leaching operations face inefficiencies due to variable ore mineralogy, oxygen availability, temperature, and operational costs, leading to suboptimal copper recovery and increased costs in mining operations.
Innovation Solution
A system utilizing predictive models trained on historical data, including mineralogy, irrigation, and temperature data, to forecast and adjust leaching operations in real-time, optimizing ore routing and process parameters for improved copper extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional ore routing based on mineralogy is used, then processing simplicity is maintained, but copper recovery efficiency deteriorates due to variable ore mineralogy and fixed processing parameters
Solution Approach 1:
The patent implements dynamic ore routing that adjusts processing parameters in real-time based on predicted copper recovery potential. The system transitions from static mineralogy-based routing to dynamic recovery-potential-based routing, allowing processing parameters to adapt continuously to varying ore characteristics and maximizing copper recovery efficiency
Solution Approach 2:
The system incorporates feedback loops where predicted copper recovery data from machine learning models informs routing decisions. Actual recovery data feeds back into the system to continuously refine predictions, creating a closed-loop control system that optimizes ore placement and processing parameters based on real-time performance
2Productivity
If more acid is provided to leach all contained copper, then copper recovery is improved, but operational cost deteriorates due to acid consumption by gangue minerals
Solution Approach 1:
The patent applies local quality by providing acid in varying amounts to different ore sections based on their specific copper content and recovery potential. High-copper sections receive sufficient acid for complete leaching, while low-copper sections receive reduced acid dosing, optimizing the balance between copper recovery and acid consumption costs
Solution Approach 2:
The system dynamically changes acid dosing parameters based on predicted copper recovery potential. By adjusting acid concentration and application rates according to real-time ore characteristics and recovery predictions, the system maximizes copper extraction while minimizing unnecessary acid consumption on low-value gangue materials
3Ease of manufacture
If heap leaching is used for secondary copper sulfides, then processing cost is reduced, but copper recovery deteriorates due to oxygen starvation in the heap interior
Solution Approach 1:
The system performs preliminary oxygenation by pre-aerating ore sections known to contain secondary copper sulfides before heap leaching begins. This preliminary action ensures adequate oxygen availability in the heap interior from the start, preventing oxygen starvation and enabling effective leaching of sulfide minerals at reduced processing costs
4Productivity
If real-time adjustment of leaching operations is implemented, then copper recovery is improved, but system complexity deteriorates due to predictive modeling and data integration requirements
Solution Approach 1:
The system implements self-service by using machine learning models to automatically predict copper recovery and make routing decisions without extensive human intervention. The system self-adjusts processing parameters based on real-time data, reducing the need for complex manual control systems while maintaining high copper recovery through automated, data-driven decisions
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances copper recovery by up to 8-16% through optimized ore placement and process adjustments, reducing operational costs and improving mining efficiency.
Implementation Method 1
training a predictive model using the historical data to create a trained predictive model
Implementation Method 2
Exposure to dilute sulfuric acid carries sufficient chemical energy to put the copper into solution
Implementation Method 3
When ferric iron oxidizes copper sulfide minerals, the ferric iron is converted to ferrous iron
Implementation Method 4
The top and sides of a heap leach stockpile are open and atmospheric oxygen is readily available
Implementation Method 5
comparing the forecast data for the mining production target to the actual data for the mining production target
Data Source
AI summary
The method may comprise receiving historical data (e.g., mineralogy data, irrigation data, raffinate data, heat data, lift height data, geographic data on ore placement and/or blower data); training a predictive model using the historical data to create a trained predictive model; adding future assumption data to the trained predictive model; running the forecast engine for a plurality of parameters to obtain forecast data for a mining production target; comparing the forecast data for the mining production target to the actual data for the mining production target; determining deviations between the forecast data and the actual data, based on the comparing; and changing each of the plurality of parameters from the forecast data to the actual data to determine a contribution to the deviations for each of the plurality of parameters.