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

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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