Digital Twin

Description Benefits Features Operation

Digital Service Solutions

Digital Twin is a cutting-edge tool developed by ME Elecmetal for the diagnosis, analysis, and optimization of SAG mills. This comprehensive solution leverages advanced computing technologies and artificial intelligence to create a real-time digital replica of the mill, providing insights into its operational status and recommending adjustments to enhance production efficiency. The tool addresses the complexities of the SAG grinding process, offering a detailed view and control to improve operational performance.

Benefits

Key Variables

Online estimation of critical operational variables such as total fill level (Jc), ball fill level (Jb), load arrangement, and liner wear.

Alerts and Recommendations

Generate real-time alerts for out-of-range variables, perform descriptive analysis to identify possible causes, and recommend adjustments based on optimization algorithms.

Control and Improvements

Facilitate improvements in operational stability, processed tonnage, media addition, and more, with seamless integration into existing control strategies.

Simulation

Simulate the response of operational variables under different boundary conditions (particle size distribution, hardness, jb/jc, % solids, ball diameter, liner profile, etc.) to verify/test new operating ranges.

Auto Calibration

Self-calibrate through periodic review of deviations and adjust parameters via the integrated DEM simulator.

Features

  • Real-Time Information: Provides crucial real-time data without disrupting mill operations, compatible with existing control systems for easy integration.
  • Non-Invasive Tool: Offers a comprehensive view of mill dynamics, including fill level measurements, internal load dynamics, and liner wear predictions.
  • Customizable Optimization: Allows for tailored process optimization based on specific client needs.

Operation

  • SAG Digital Twin is fed by online process data and utilizes online process data to develop operational variables through phenomenological models, optimization algorithms, and AI.
  • These data are used to diagnose mill performance, support control strategies to enhance stability, and maximize productivity.
  • Additionally, the tool allows simulation of the mill’s response under different conditions and performs self-calibration to maintain accuracy and effectiveness over time.

Solutions

ME FIT Programs

Products and services

Mining 4.0

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