Chapter (22): Machine Learning Methods for Continuous Material Stream Characterization

CHAPTER CONTENT

  • Introduction to/Overview of material stream characterization in mining
    • Discontinuous vs. continuous characterization
    • Laboratory testing / Offline / Online / Inline testing
    • Innovation potential / characterization as a requirement for future automation of processes
  • Fundamentals of the Acoustic Emission Technology (AET)
    • Terminology / general introduction to Acoustic Emission Technology
    • Typical fields of application
  •  Signal processing and machine learning with focus on material stream characterization
    • Analysis of AE signals
    • Machine learning introduction (if not done in a previous chapter in general)
    • Support vector machine introduction (if not done in a previous chapter in general)
  • Development of the methodology in lab scale environments
    • General approach using AE for material stream characterization
    • Different lab setups for dry and wet mining
    • Lab test results and conclusions
  • Validation of methodology in various field tests
    • Field test in gypsum quarry (setup, analysis, results)
    • Field test in lignite mine (setup, analysis, results)
  • Conclusion and outlook on potential fields of application and research
    • Conclusion of current state of development
    • Improvements on current methodology
    • Considerable fields of application in mining
    • Future research with focus on automation
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Chapter Includes

  • 6 Parts
  • 1 Form