Deep Raffinate Injection Control for Ore Placement Leaching
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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 environmental data, to optimize ore placement and leaching processes, adjusting parameters in real-time to enhance copper recovery and reduce costs.
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 optimization is limited
Solution Approach 1:
The system changes the routing parameters from static mineralogy-based classification to dynamic parameters including real-time leach analytics, oxygen availability, temperature, and predictive model outputs. This allows optimization of copper recovery by adjusting which ores are routed to leaching based on multiple varying parameters rather than fixed mineralogy categories.
Solution Approach 2:
The system implements feedback loops where leach analytics data and actual copper recovery results are continuously fed back into the predictive models and routing decisions. This closed-loop feedback enables the system to learn from past performance and continuously optimize ore routing to maximize copper recovery while adapting to changing ore characteristics and leaching conditions.
2Productivity
If deep raffinate injection is activated for all ore sections, then copper recovery from oxygen-starved zones is improved, but operational costs increase
Solution Approach 1:
The system applies deep raffinate injection selectively only to specific ore sections where predictive models identify oxygen starvation and high copper content. Rather than uniformly applying the treatment across the entire leach pad, the system creates localised treatment zones where it is most effective, thereby improving copper recovery from deep zones while minimising unnecessary operational costs in areas where injection would not provide benefit.
Solution Approach 2:
The predictive models perform preliminary analysis of ore placement data, mineralogy, and leach analytics to identify which sections will benefit from deep raffinate injection before the injection occurs. This advance identification allows the system to pre-plan and selectively activate injection only in high-value target zones, avoiding wasteful expenditure on sections where the treatment would not significantly improve copper recovery.
3Productivity
If selective ore placement based on predictive models is implemented, then leaching efficiency is optimized, but data processing complexity increases
Solution Approach 1:
The predictive model system is designed to handle multiple functions within a unified framework: it processes ore placement data, mineralogy information, leach analytics, and environmental parameters simultaneously to generate routing decisions. This multi-functional approach consolidates what could be separate complex systems into a single integrated platform, optimising leaching efficiency while managing data processing complexity through consolidation rather than multiplication of separate systems.
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
Improves copper recovery by up to 8-16% through optimized ore routing and leaching processes, reducing operational costs and increasing mine life by optimizing ore reserves.
Implementation Method 1
training a predictive model using the historical data to create a trained predictive model
Implementation Method 2
When ferric iron oxidizes copper sulfide minerals, the ferric iron is converted to ferrous iron. The ferrous iron is converted back to ferric iron to further oxidize copper sulfide minerals
Implementation Method 3
Air or oxygen may either be introduced by physically piping or blowing it into the ore structure
Implementation Method 4
Exposure to dilute sulfuric acid carries sufficient chemical energy to put the copper into solution
Implementation Method 5
Leach recoveries from oxide and carbonate minerals can approach 100% of the contained copper, provided sufficient acid is available for leach reactions
Implementation Method 6
When ferric iron oxidizes copper sulfide minerals, the ferric iron is converted to ferrous iron. The ferrous iron is converted back to ferric iron to further oxidize copper sulfide minerals
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.