Mining Equipment Capacity Planning Using Entropy-Based Cycle Time Prediction
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Solution Overview
Problem
Current methods for determining equipment capacities in mining operations to meet production targets are often empirical and inefficient, leading to either insufficient or excessive equipment usage, and fail to account for operational variations and entropy effects, resulting in inconsistent achievement of desired production levels.
Innovation Solution
A system and method that estimate the effect of entropy on cycle times and processing times using historical data, predict potential delays, and determine the required capacities of loading and conveying systems, such as shovels and haul trucks, to accurately deploy equipment and meet defined production targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If additional shovels and haul trucks are provided to meet production targets, then production capacity is improved, but resource efficiency deteriorates due to excessive equipment and increased idle times
Solution Approach 1:
The system performs preliminary estimation of equipment requirements by analyzing historical cycle time data and predicting future cycle times using entropy effects before deploying equipment. This allows the mining operation to determine the optimal number of shovels and haul trucks needed in advance, avoiding both insufficient capacity and excessive equipment deployment that would lead to idle times and resource waste.
Solution Approach 2:
The system continuously monitors actual cycle times and compares them with predicted cycle times, using this feedback to refine future predictions and equipment deployment decisions. By incorporating actual operational data and entropy effects into the prediction model, the system adapts to changing conditions and optimizes equipment utilization dynamically, preventing both production shortfalls and resource inefficiency.
2Ease of manufacture
If equipment capacities are determined using empirical methods, then implementation is simplified, but accuracy deteriorates leading to inconsistent achievement of production targets
Solution Approach 1:
The system replaces empirical, trial-and-error methods with a computational prediction model that uses historical cycle time data and entropy effects to determine equipment capacities. This substitution of mechanical/empirical approaches with information-based computational methods provides both accuracy in predicting equipment requirements and automated simplicity in implementation, eliminating the need for extensive operational trials.
3Speed
If the number of haul trucks is increased to prevent congestion, then material flow is improved, but wait times deteriorate due to excessive trucks causing traffic congestion
Solution Approach 1:
The system predicts future cycle times for haul trucks based on historical data and entropy effects before deploying the fleet. By determining the optimal number of trucks in advance based on predicted performance rather than reacting to observed congestion, the system achieves smooth material flow without excessive trucks that would cause wait times and traffic congestion.
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.