Abstract:

Purpose: Many underground mines today have started tracking real-time data about their equipment and operator. This paper will identify factors that influence trucking efficiency in Underground loading and hauling operations and show how they impact productivity with real examples.

Methodology:

Real-time data collected by mines is becoming increasingly crucial for the industry. Various data such as production, quality, and safety data affect trucking efficiency (for example, equipment data, waiting time data, time lost due to inadequate training, accidents, etc.). It is necessary to have a system that can collect this data and store it in a way that is accessible and easy to use. It's also essential to have a system that can make sense of this data as a predictive tool for the future of these mines. This paper will look at a few scenarios from actual mines, discuss various data types that can be collected and interpreted, and present the actions and results based on this data. This paper also attempts to look at the time utilization model for these mines to highlight the top reasons that cause delays or downs or idle equipment.

Results:

The three main reasons for trucking delays in the underground loading and hauling operations are technical difficulties, worker issues, and scheduling issues. Mine production targets must be carefully developed, with an assessment of all factors affecting production and the mine's profitability. Real-time data provides the ability to assess common causes of delays, enables a mine to build in contingencies in the plan, and provides key stakeholders and investors with an accurate picture of the mine's likely production.

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Satish Penmetsa