Mining Equipment Capacity Planning Under Cycle-Time Variability
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Solution Overview
Problem
Existing methods for determining the optimal capacities of equipment, such as shovels and haul trucks, in mining operations are empirical and require significant trial and error, often failing to consistently meet production targets due to changing mining environments and operational variations.
Innovation Solution
A method and system utilizing a production constraint module, cycle time and variance module, processing system performance module, and delay hazard function module to estimate the effects of entropy on cycle times and processing times, predict potential delays, and determine the required capacities of loading and conveying systems to meet defined production targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If additional numbers of shovels and haul trucks are provided to achieve desired production targets, then production capacity is improved, but resource efficiency deteriorates due to excessive equipment capacity and increased idle times
Solution Approach 1:
The system performs preliminary stochastic simulation and analysis to determine the optimal number of shovels and haul trucks before deployment. By simulating various equipment configurations and their expected performance under uncertain conditions, the system identifies the minimum equipment capacity needed to meet production targets with a specified confidence level, avoiding both insufficient and excessive equipment provisioning.
Solution Approach 2:
The system changes the parameter of equipment capacity from fixed empirical values to dynamically determined values based on stochastic simulation results. By adjusting equipment numbers based on simulated production targets, confidence levels, and operational constraints, the system optimizes the balance between productivity and resource efficiency.
2Ease of manufacture
If empirical methods are used to determine equipment capacities, then implementation simplicity is improved, but measurement precision deteriorates due to lack of accurate production target achievement
Solution Approach 1:
The system incorporates feedback loops where stochastic simulation results feed into equipment capacity determination, which then informs production planning and execution. The simulation continuously refines equipment capacity recommendations based on simulated performance data, creating a feedback mechanism that improves measurement precision while maintaining automated implementation.
Solution Approach 2:
The system replaces manual empirical estimation methods with automated stochastic simulation and computational analysis. By substituting human judgment and trial-and-error approaches with computer-based simulation that accounts for uncertainty and variability, the system achieves both implementation simplicity through automation and measurement precision through rigorous statistical analysis.
3Reliability
If equipment capacities are increased to account for operational variations, then reliability is improved, but device complexity increases due to larger equipment fleets and coordination requirements
Solution Approach 1:
The system transitions from static equipment capacity planning to dynamic capacity determination based on stochastic simulation. By modeling operational variations and uncertainties dynamically, the system determines equipment capacities that maintain reliability while adapting to changing conditions, avoiding the need for excessively large equipment fleets to cover all possible scenarios.
Data Source
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AI summary
Methods and systems determine at least one production constraint for a material loading system and a material processing system; estimate an effect of entropy on a cycle time of a material conveying system to produce a future cycle time estimate; estimate an effect of entropy on a material processing time to produce a future material processing time estimate; predict whether a delay will occur during the operation of the material loading, material conveying, and material processing systems; estimate a duration of the predicted delay; determine a loading system capacity and a conveying system capacity required to meet the defined production target based on the production constraint and estimates of the future cycle time, the future material processing time, and the duration of the predicted delay; and deploy one or more material loading and conveying systems to meet the determined loading and conveying system capacities.