Discrete Event Simulation for Mill Reline Optimization
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Solution Overview
Problem
Current methods for estimating mill reline times are crude and lack the ability to predict the effects of small changes, leading to inefficiencies and high economic risks during mineral processing plant shutdowns, as they rely on historical data and manual experience without sophisticated predictive capabilities.
Innovation Solution
A discrete event simulation system that collects and analyzes mill relining data from multiple sites, treating variables as discrete time/frequency distributions to simulate and optimize the mill reline process, determining optimal workflows and benchmarking performance across different sites and designs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If discrete event simulation software is used to model mill reline processes, then prediction accuracy and ability to analyze small changes is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The mill reline process is divided into thousands of discrete activities and tasks that can be individually modeled and simulated. Each activity is treated as a separate event in the discrete event simulation, allowing precise tracking and analysis of process variations without requiring a monolithic complex system.
Solution Approach 2:
A virtual digital twin of the mill reline process is created through simulation software that replicates the physical process. This digital copy allows for accurate prediction and analysis of small changes without affecting the actual physical system, reducing the need for complex physical measurement infrastructure.
2Ease of manufacture
If historical data and manual experience methods are used for estimating reline times, then implementation simplicity is maintained, but prediction accuracy and ability to maximize plant efficiency deteriorates
Solution Approach 1:
The simulation system incorporates feedback loops that continuously refine predictions based on simulated performance data. The system analyzes the interplay between thousands of discrete activities and provides feedback on how changes affect overall reline time, enabling continuous optimization of plant efficiency while maintaining ease of use through automated calculations.
Solution Approach 2:
The system allows easy modification of input parameters such as liner size, quantity, crew proficiency, equipment characteristics, and plant design variables. By changing these parameters in the simulation, users can quickly assess their impact on reline time and plant efficiency without complex manual recalculations, maintaining simplicity while improving predictive accuracy.
3Speed
If average time per piece based on historical data is used to estimate reline times, then calculation speed is maintained, but ability to predict effects of small changes and maximize efficiency deteriorates
Solution Approach 1:
The simulation system dynamically models the interrelationships between thousands of discrete activities rather than using static average values. This dynamic approach allows the system to quickly recalculate and predict the effects of small changes in liner specifications, crew composition, or equipment configuration while maintaining fast calculation speeds through optimized simulation algorithms.
Solution Approach 2:
The system enables rapid parameter changes and immediate recalculation of reline time estimates. Users can modify input parameters such as liner dimensions, quantity, or operational variables, and the simulation quickly produces updated predictions, maintaining calculation speed while dramatically improving adaptability to different scenarios and change analysis.
Data Source
AI summary
Machine-implemented methods, systems, processing devices and machine-readable media are provided for simulating a mill reline, which involve collecting mill relining data and processing the relining data as simulation parameters using a mill relining model. The relining data generally include variables treated as discrete time/frequency distributions. Simulated events for the mill reline may be generated based on the relining data. Additional relining data relating to a specific existing site may be included as simulation parameters. The simulation may be used in determining an optimal work flow for carrying out the mill reline at a specific existing site, e.g., where time to carry out the mill reline is minimized. The simulation may also be employed for benchmarking. The additional relining data may be provided in video format, and analysis of the video format data may be undertaken, e.g., by an operator for refining and/or verifying the additional relining data, prior to utilizing the additional relining data as simulation parameters in the simulation model. The mill relining data may also include additional parameters, such as the size and quantity of the liners, the reline planning process and relining crew proficiency, the relining equipment and plant design surrounding the mill being relined, and the like.