Entwicklung und Anwendung eines innovativen Konzepts zur Inline-Charakterisierung von Stoffgemischen in kontinuierlichen Massenströmen mittels der Acoustic Emission Technologie

The global mining industry is facing the challenge of satisfying an increasing demand of primary raw materials, despite decreasing cut-off grades and deposits, which get more and more complex. Strategies for a sustainable and technology-based development of mining methods are demanded globally by societies and policy makers – Germany included. The development and implementation of innovative technologies and processes is an important building block for reaching those requirements. These technologies aim at improving the efficiency of extraction, the transportation, the quality management, the loading/dumping as well as the processing of materials. Regarding the aspects of efficiency improvement and resource optimization, future mining and processing will work increasingly on a (partly-)autonomous basis. The necessary growth of underground exploitation is accompanied by increasing risks and danger. Therefore, the use of innovative technologies shall improve the safety for staff and machinery by minimizing the employees’ exposition to dangerous areas. Due to the devolvement of mining methods and innovative technologies, challenges connected to the transportation and characterization of material flows become more and more important. Up-to-date data of material composition is an enabling technology for an autonomous distribution and a more efficient production. Just in case of this, an optimized processing in downstream plants or a quality dependent dumping is possible. An innovative characterization method of material flows based on the acoustic emission technology is presented within this thesis. With the help of this new technology, the continuous and indirect characterization of material flows becomes possible. This has not been achieved with known methods yet. Besides a description of the current state of research for characterizing mineral resources, the fundamentals of the acoustic emission technology and processes for evaluating material flow are presented. This thesis focusses on the evaluation of machine learning methods for material flow characterization. Feasibility studies were carried out at laboratory scale. These tests included artificial and idealised mass flows, as well as real-world samples. The developed algorithms proved their feasibility in field tests conducted at industrial sites. Based on the gained experience and the requirements collected from industry partners, a concept of a possible inline measurement system, which characterizes material flows in the mineral resources industry, is presented. The thesis finishes with a summary of relevant results and gives an outlook on future areas of application as well as possible aspects of optimization