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The moisture content of aggregates is a crucial factor in construction, particularly in concrete mixing, as it influences the total water required in the mix. Moisture content represents the water coated on the exterior surface of the aggregate existing in a saturated and surface-dry condition. The total water content of a moist aggregate is the sum of its moisture content and water absorption.
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Moisture Determination for Fine-Sized Copper Ore by Computer Vision and Thermovision Methods.

Dariusz Buchczik1, Sebastian Budzan1, Oliwia Krauze1

  • 1Department of Measurements and Control Systems, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, 44-100 Gliwice, Poland.

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Summary

This study developed computer vision and thermovision methods for estimating copper ore moisture content on-site. Vision images effectively estimate moisture up to 5%, while thermography requires advanced processing.

Keywords:
computer visioncopper oregrindingmodelingmoistureparticle sizepowdersthermovision

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Area of Science:

  • Materials Science
  • Mineral Processing
  • Computer Vision

Background:

  • Bulk material moisture content significantly affects dry grinding efficiency, particle characteristics, and powder transport.
  • Accurate on-site moisture estimation is crucial for optimizing industrial processes like dry grinding.

Purpose of the Study:

  • To develop and evaluate computer vision and thermovision techniques for real-time copper ore moisture estimation.
  • To investigate the impact of particle size on moisture estimation accuracy.

Main Methods:

  • Utilized computer vision and thermovision imaging (standard and macro scales) on copper ore (0-2 mm particle size, 0.5-11% moisture).
  • Developed and cross-validated models for moisture estimation based on image analysis.
  • Analyzed the influence of particle size on estimation results.

Main Results:

  • Median-intensity vision images showed a monotonic relationship with copper ore moisture content up to approximately 5%.
  • Thermograms required sophisticated computer vision processing, not just mean temperature analysis, for effective moisture estimation.
  • Particle size was found to influence the accuracy of moisture estimation.

Conclusions:

  • Computer vision offers a viable method for on-site copper ore moisture estimation, particularly for lower moisture ranges.
  • Advanced image processing is necessary for accurate moisture determination using thermovision data.
  • The developed techniques can aid in optimizing dry grinding operations by providing real-time moisture feedback.