Mining Equipment Deployment Using Stochastic Delay Prediction

Solution Overview

Problem

Current methods for determining the optimal equipment capacities for mining operations to meet production targets are often empirical and require significant trial and error, leading to inefficiencies and inconsistent performance due to changing mining conditions.

Innovation Solution

A method involving stochastic simulations to estimate the effects of entropy on cycle times and processing times, predicting delays, and determining required loading and conveying system capacities to meet defined production targets.

Engineering Contradictions & Design Principles

VSEngineering 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

Engineering Contradiction:

Improveproduction target achievementVSAvoidinefficiency and resource waste

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

The system performs preliminary stochastic simulations using historical data to predict future cycle times and identify potential delays before equipment is deployed. This allows the optimal number of shovels and haul trucks to be determined in advance, avoiding both insufficient capacity and excessive equipment that would cause congestion and idle times.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system continuously monitors actual operational data and compares it with simulated predictions, using this feedback to refine future equipment deployment decisions. This closed-loop approach ensures that equipment numbers are consistently optimized to meet production targets without waste.

Inventive Principle:

Principle #23Feedback

2Ease of manufacture

If empirical methods are used to determine equipment capacities, then the process is simpler to implement, but significant trial and error is required and optimal capacities are rarely reached due to constantly changing mining conditions

Engineering Contradiction:

Improveease of implementationVSAvoidconsistency in meeting production targets

Core Design Contradiction:

Ease of manufactureVSReliability

Solution Approach 1:

The system performs preliminary stochastic simulations using historical data to predict future cycle times and identify potential delays before equipment is deployed. This allows the optimal number of shovels and haul trucks to be determined in advance, avoiding both insufficient capacity and excessive equipment that would cause congestion and idle times.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system accounts for changing mining conditions by using stochastic simulations that incorporate variability in cycle times, processing times, and delay probabilities. This allows equipment capacities to be dynamically optimized for different operational scenarios rather than relying on static empirical rules.

Inventive Principle:

Principle #35Parameter changes

3Reliability

If more equipment is deployed to account for operational variations and delays, then production targets can be consistently met, but resource utilization becomes sub-optimal and costs increase

Engineering Contradiction:

Improveconsistency in meeting production targetsVSAvoidequipment capacity

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The system performs preliminary stochastic simulations using historical data to predict future cycle times and identify potential delays before equipment is deployed. This allows the optimal number of shovels and haul trucks to be determined in advance, avoiding both insufficient capacity and excessive equipment that would cause congestion and idle times.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system replaces traditional mechanical trial-and-error approaches with computational stochastic simulations. By using probability-based models to predict cycle times and delays, the system can determine precise equipment requirements without needing to deploy excess equipment as a buffer, thus optimizing resource utilization while ensuring production targets are met.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250190897A1Methods and systems for deploying equipment required to meet defined production targets

Publication Date: 2025.06.12 FREEPORT MCMORAN INC

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.