Heap Leach - Executive Summary
Airth Act
Airth Act is a powerful solution that utilizes advanced analytics and machine learning to drive improvements in mining operations through data-driven predictions and recommendations.
With Airth Act, mining companies can leverage data-driven insights to optimize operations, driving value and increasing profitability. By utilizing advanced analytics and machine learning, Airth Act empowers mining companies to identify opportunities for improvement that would otherwise be difficult or impossible to detect.
In summary, Airth Act is an essential tool for mining companies looking to stay ahead of the curve and succeed in the competitive mining industry. By offering real-time recommendations and data-driven insights, Airth Act helps mining companies enhance operational efficiency, reduce costs, and maximize production output, ultimately driving value and increasing profitability.
Heap Leach
Airth Act Heap Leach Recovery Modeling solution provides a powerful tool for improving financial forecasting and budgeting in mining operations. By connecting to mine planning and metallurgical data, the solution generates and compares different recovery scenarios, leveraging machine learning to calibrate model parameters.
With its user-friendly interface, the solution enables stakeholders to view approved scenarios, evaluate production, and track inventory. This enhances decision-making and enables mining companies to optimize their operations for improved financial outcomes.
At Airth, we understand the importance of accuracy in mining operations. That’s why we offer comprehensive support to ensure accurate results and seamless integration with existing systems. By leveraging the Heap Leach Recovery Modeling solution, mining companies can improve their financial forecasting and budgeting, ultimately driving value and increasing profitability.
MODELING CONSIDERATIONS
- Agnostically connect to mine planning and metallurgical data covering K factor, scale-up factor, and inventory calculations through empirical formulas and assumptions such as CIC, Barren, and pond volume.
- Use Column leach, IBRT, PSD test and Percolation results for model influencing.
SCENARIO ANALYSIS
- Generate and compare different recovery scenarios based on mine scheduling or process alternatives.
- Categorize scenario types for budget or forecast with action/approval option.
MODEL CALIBRATION
- Metallurgical data analysis tool to evaluate production.
- Leverage machine learning to calibrate model parameters based on historical leaching performance.
- Visualize and evaluate the calibration history and associated deviations
AUDITABILITY
- Quickly chart any scenario inputs and calculations for all-in-one validation and improved modeling confidence.
REPORTING
- Allow all stakeholders to view the approved scenario’s actual, budgeted, and forecasted metal production figures including inventory tracking with extracted and pour values.
TECHNICAL SUPPORT
- Airth offers support for our solutions and can help analyze and identify data discrepancies and model results.
Heap Leach - Scenario Analysis
Heap Leach - Data Input
Heap Leach - Detailed Analysis
Heap Leach - Model Calibration
Persona Based Portal
Configurable Accounts Settings
Integrated Support System
Process Auditability
Ondemand License Availabilty
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Predictive Ore Reconciliation
Airth Act Predictive Ore Reconciliation solution is a powerful tool that utilizes machine learning to predict production model results ahead of blasthole sampling analysis. By analyzing historically backed grade qualities for benches below the active ore control bench, the solution provides more representative operational sampling results for improved planning.
The solution also evaluates ore waste boundaries and predicts contact adjustments for improved blast designs. This enables mining companies to optimize their operations, reduce risk, and identify areas for re-evaluation. In addition, the solution uses heat mapping to identify areas of uncertainty, enabling more accurate and efficient decision-making.
With balanced shovel planning and evaluation of resource model performance, the Predictive Ore Reconciliation solution enhances operational efficiency, reduces risk, and drives value for mining companies. By predicting production model results ahead of time, mining companies can make more informed decisions and optimize their operations for improved outcomes.
In summary, the Predictive Ore Reconciliation solution is a valuable asset for mining companies looking to enhance their operations and reduce risk. By utilizing machine learning to predict production model results, the solution provides more representative operational sampling results, identifies areas of uncertainty, and enables more accurate decision-making. With balanced shovel planning and resource model performance evaluation, the solution drives value and improves outcomes for mining companies.
COMPARE MODELS
- Connect to resource and ore control models to start machine learning evaluation
CONTACT RESOLUTION
- Evaluate ore waste boundaries that have more resolution than the resource model before operational sampling results
- Predict contact adjustment could improve blast designs for ore/waste separation
REPORT PERFORMANCE
- Evaluate the performance of the resource model with regards to the production model
- Identify area for modeling re-evaluation
PREDICT QUALITIES
- Analysis of historically backed grade qualities for benches below active ore control bench
- New qualities will have a high chance of being more representative of operational sampling results
- Qualities can then be used for improved planning
RISK ASSESSMENT
- Use model heat mapping to see areas of uncertainty
- Balance areas of varying confidence in shovel planning to minimize
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