Ore Placement Forecasting for Deep Raffinate Injection 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 environmental factors, 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 efficiency deteriorates due to variable ore placement and leaching conditions
Solution Approach 1:
The system performs preliminary analysis of ore mineralogy and placement characteristics before leaching begins. Historical data is pre-processed and stored, and predictive models are trained in advance to enable optimized routing decisions from the start of operations
Solution Approach 2:
The system implements continuous feedback loops where actual leaching performance data is compared against predictive model forecasts. This feedback is used to retrain and refine the predictive models, which then adjust ore routing recommendations in real-time to maximize copper recovery while accounting for varying ore characteristics and leaching conditions
2Productivity
If acid is provided to leach all contained copper, then copper recovery is maximized, but operational cost deteriorates due to excessive acid consumption on gangue minerals
Solution Approach 1:
The system applies different acid dosing strategies to different zones and ore types within the heap leach operation. Based on predictive models that analyze local ore mineralogy and leaching characteristics, acid is optimized for each specific location rather than applying uniform dosing across the entire operation
Solution Approach 2:
The system dynamically adjusts leaching parameters including acid concentration, application rate, and timing based on real-time monitoring of leaching progress and ore characteristics. This allows optimization of acid usage to match actual copper release rates and minimize consumption on gangue minerals
3Productivity
If ore is placed without optimization, then placement simplicity is maintained, but copper recovery deteriorates due to suboptimal ore placement affecting leaching efficiency
Solution Approach 1:
The system performs preliminary analysis of ore mineralogy and placement characteristics before leaching begins. Historical data is pre-processed and stored, and predictive models are trained in advance to enable optimized routing decisions from the start of operations
Solution Approach 2:
The system implements continuous feedback loops where actual leaching performance data is compared against predictive model forecasts. This feedback is used to retrain and refine the predictive models, which then adjust ore routing recommendations in real-time to maximize copper recovery while accounting for varying ore characteristics and leaching conditions
4Productivity
If leaching operations are not adjusted based on analytics, then operational simplicity is maintained, but copper recovery deteriorates due to inability to adapt to varying ore and environmental conditions
Solution Approach 1:
The system transitions from static leaching operations to dynamic control where parameters such as acid dosing rates, irrigation patterns, and ore routing decisions are continuously adjusted based on real-time data from sensors and predictive model forecasts
Solution Approach 2:
The system implements continuous feedback loops where actual leaching performance data is compared against predictive model forecasts. This feedback is used to retrain and refine the predictive models, which then adjust ore routing recommendations in real-time to maximize copper recovery while accounting for varying ore characteristics and leaching conditions
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
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.