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Deep Learning Approach at the Edge to Detect Iron Ore Type.

Emerson Klippel1,2, Andrea Gomes Campos Bianchi3, Saul Delabrida3

  • 1Graduate Program in Instrumentation, Control and Automation of Mining Processes, Instituto Tecnológico Vale, Federal University of Ouro Preto, Ouro Preto 35400-000, Brazil.

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Summary
This summary is machine-generated.

Edge artificial intelligence (AI) detects iron ore landslide risk using conveyor belt images. This cost-effective system achieves 91% accuracy, improving safety in beneficiation plants.

Keywords:
AIoTDNNedge AIiron ore quality

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Area of Science:

  • Mining Engineering
  • Artificial Intelligence
  • Geotechnical Engineering

Background:

  • Iron ore transfer in beneficiation plants carries a constant risk of material collapse.
  • Current monitoring instrumentation is expensive, complex, and difficult to maintain.
  • Early detection of potential landslides is crucial for operational safety and efficiency.

Purpose of the Study:

  • To propose and evaluate an edge artificial intelligence (AI) system for early landslide risk detection in iron ore beneficiation.
  • To develop a cost-effective and maintainable solution compared to existing instrumentation.
  • To assess the feasibility of deep learning models deployed at the device edge for real-time monitoring.

Main Methods:

  • Defined device edge parameters and a deep neural network (DNN) model for image analysis.
  • Developed a prototype system for collecting and training the AI model with iron ore images.
  • Compressed the DNN model for efficient deployment on the edge device.
  • Integrated a real-time clock for synchronizing image data with plant process information.

Main Results:

  • Field tests demonstrated the prototype's effectiveness under operational conditions.
  • The AI model achieved a detection accuracy of 91% and a recall of 96%.
  • Synchronization with process information ensured accurate image classification by specialists.

Conclusions:

  • Edge AI offers a feasible and accurate solution for detecting iron ore landslide risk.
  • The developed system provides a cost-effective and low-maintenance alternative to traditional instrumentation.
  • This approach enhances safety by enabling early detection and prevention of material avalanches during ore transfer.