Mining Equipment Deployment Using Stochastic Cycle-Time Prediction
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Solution Overview
Problem
Mining operations face inefficiencies in determining the optimal number of shovels and haul trucks required to meet production targets, often resulting in insufficient or excessive equipment capacity due to empirical methods that fail to account for operational variations and entropy effects.
Innovation Solution
A method using stochastic simulations based on historical data to estimate cycle times, processing times, and delays, allowing for accurate determination of loading and conveying system capacities to meet defined production targets, and deploying equipment accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If additional numbers of shovels and haul trucks are provided to achieve the desired production target, then the production target can be more easily achieved, but inefficiencies occur and resources are sub-optimally used due to increased wait or idle times and traffic congestion
Solution Approach 1:
The system performs stochastic simulations using historical data to predict future cycle times, processing times, and delays before equipment deployment decisions are made. This preliminary analysis allows optimization of equipment numbers to avoid both insufficient capacity and excessive wait times, resolving the contradiction by determining the optimal equipment quantity in advance rather than through trial and error
Solution Approach 2:
The system continuously collects historical operational data and uses it to refine stochastic simulation models, creating a feedback loop that improves prediction accuracy over time. This enables dynamic adjustment of equipment deployment strategies to consistently achieve optimal balance between production targets and minimization of wait times
2Productivity
If additional numbers of shovels and haul trucks are provided to achieve the desired production target, then the production target can be more easily achieved, but resource utilization becomes sub-optimal due to excessive equipment capacity
Solution Approach 1:
The system performs stochastic simulations using historical data to predict future cycle times, processing times, and delays before equipment deployment decisions are made. This preliminary analysis allows optimization of equipment numbers to avoid both insufficient capacity and excessive wait times, resolving the contradiction by determining the optimal equipment quantity in advance rather than through trial and error
Solution Approach 2:
The system uses stochastic simulation to model various equipment quantity scenarios and their impact on production targets and resource utilization. By analyzing probability distributions of cycle times and delays, the system identifies the optimal equipment quantity that achieves production targets while minimizing resource waste, transforming the approach from empirical to data-driven parameter optimization
3Ease of manufacture
If empirical methods are used to determine equipment capacities, then the approach is simple to implement, but significant operational trial and error is required before arriving at optimal capacities
Solution Approach 1:
The system replaces empirical trial-and-error methods with stochastic simulation based on historical data. By using computational models to predict future operational parameters, the system eliminates the need for extensive physical trial and error while providing more accurate and reliable equipment capacity determination
Solution Approach 2:
The system creates virtual copies of the mining operation through stochastic simulation, using historical data to model future scenarios. This allows testing and optimization of equipment capacities in a virtual environment before implementation, significantly reducing the time and resources required for trial and error
4Loss of energy
If optimal equipment capacities are determined, then resource utilization is optimized, but the optimal capacity is rarely reached and consistently met because the mining environment is constantly changing
Solution Approach 1:
The system transitions from static equipment capacity determination to dynamic optimization by continuously collecting historical operational data and updating stochastic simulation models. This allows the system to adapt to changing mining conditions over time, maintaining optimal resource utilization as the environment evolves
Solution Approach 2:
The system continuously collects historical operational data and uses it to refine stochastic simulation models, creating a feedback loop that improves prediction accuracy over time. This enables dynamic adjustment of equipment deployment strategies to consistently achieve optimal balance between production targets and minimization of wait times
Data Source
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.