This master thesis is performed in a collaboration with VA Erzberg mining company ¿ the largest open pit hard rock mine in central Europe. According to this fact the extraction processes are being performed 24 hours and 7 days a week. Thus, a fatigue monitoring system is planning to be implemented in order to mitigate dangerous situations during night shifts. In order to do so an investigation of possible systems on market and their comprehensive evaluation has been conducted. This paper work provides a suggestion of one company (producer or provider of a technology) that better fits in terms of quality, costs and efficiency. Another task is the optimization of a traffic flow control in the mine. A solution on how to improve the current truck dispatch system has been developed. The improvement of the current system is performed with a Python script. The program conducts an analysis of every dump truck¿s movements during the whole day and creates a report. It includes several graphs with the crucial metrics and a brief overview of every machine performance. Based on this information the mine management can check the results of every truck¿s work and make necessary optimizations. The study involves a comprehensive review of the literature on data analysis and fatigue monitoring systems in the mining industry, as well as case studies of companies that have successfully applied these technologies to their operations. The findings of the study suggest that data analysis has the potential to improve production planning and optimize resource utilization. Meanwhile fatigue control systems can enhance safety and environmental management in the mining industry. In conclusion, this study demonstrates that data analysis can play a vital role in improving the competitiveness and sustainability of the mining industry.
|Translated title of the contribution||Bewertung und Optimierung eines 24/7-Bergwerks aus sicherheitstechnischer, betrieblicher und organisatorischer Sicht|
|Award date||31 Mar 2023|
|Publication status||Published - 2023|
Bibliographical noteno embargo
- open pit
- data analysis