547
4 CASE STUDY
This paper is a case study in a mine located in the iron quadrangle, the “Casa de Pedra” mine,
a division of CSN Group, located in the city of Congonhas, in the Brazilian state of Minas
Gerais.
The company stands out for the extraction of iron ore of high content and for having an
integrated system of distribution of its production formed by [mine—railroad—port] that
supports the attendance of all the current operations from the production, transport and
shipment of the ore for export. The method of mining is open pit, with mining of the waste
material; drilling, rock clearing, ore loading and transportation. The extraction is carried out
in descending order, in horizontal levels, in benches of 13 m of height and berms of 8 m of
width. The slope angles vary between 36 and 45° depending on the lithology and relevant
geotechnical parameters.
The objective of the research is to simulate three different mine scenarios, between operational improvements and autonomous operation. The results obtained will be analyzed
through mine operational indicators, such as: Transported mass (t); Utilization (%) and Productivity (t/h) of load and transportation equipment; Index of queues in loading (%); Idle
index of loading equipment (%).
The technology used by the manufacturers of autonomous equipment uses presence sensors
and positioning projection, as well as analyzing the information of the embedded telemetry
system. Occurrences such as high braking rate, pressure variation between the suspensions
and high rolling resistance can cause speed reduction and even equipment shutdown.
For the implementation of a future project of autonomous equipment in mining, it is
necessary to avoid possible problems that the technology can cause. Unnecessary equipment
stops can occur in situations where the technology identifies risks to the equipment. The
main factors to be analyzed at the beginning of the project are:
• Width of loading locations;
• Construction, design and maintenance of access roads;
• Clovers and crosses;
• Impact of the presence of auxiliary equipment in the mine (tractors, motor graders,
vans);
• Drainage plan;
• Operational interventions (displacements, quality control, detonations, among others).
The work of adequacy of these mine parameters is fundamental to maximize the future
results of the implementation of an autonomous mine project.
5 RESULTS
The results of the simulations can be verified in Figures 4, 5 and 6.
Simulation 2 resulted in a 5% increase in the mass moved in the simulated period of one
day of operation and a 4% reduction in the effective use of transport equipment. Simulation
3 was 16% more productive, with a 6% increase in the actual use of the trucks.
The results related to the transport equipment can be verified in Figure 5. It can be analyzed that the indicator of queues in the load rises as the mass increases. This means that
there is an excess of trucks used in the operation and, therefore, there is a possibility of
reducing the transport fleet. This effect related to queues can also be verified in transport
productivity. The indicator increases 6% in simulation 2, but reduces 2% in comparison with
the autonomous scenario, influenced by the increase of the queues.
The use of the load equipment increases 17% with the implementation of the autonomous
equipment fleet. This result shows that the current operation, characterized by simulation 1,
has a larger dimensioning in loading and excessive equipment available. The loading productivity increased 25%, influenced by the 31% reduction in equipment idleness.
Table 3 shows the final result of the tests, with the comparison of the simulated scenarios.
4 CASE STUDY
This paper is a case study in a mine located in the iron quadrangle, the “Casa de Pedra” mine,
a division of CSN Group, located in the city of Congonhas, in the Brazilian state of Minas
Gerais.
The company stands out for the extraction of iron ore of high content and for having an
integrated system of distribution of its production formed by [mine—railroad—port] that
supports the attendance of all the current operations from the production, transport and
shipment of the ore for export. The method of mining is open pit, with mining of the waste
material; drilling, rock clearing, ore loading and transportation. The extraction is carried out
in descending order, in horizontal levels, in benches of 13 m of height and berms of 8 m of
width. The slope angles vary between 36 and 45° depending on the lithology and relevant
geotechnical parameters.
The objective of the research is to simulate three different mine scenarios, between operational improvements and autonomous operation. The results obtained will be analyzed
through mine operational indicators, such as: Transported mass (t); Utilization (%) and Productivity (t/h) of load and transportation equipment; Index of queues in loading (%); Idle
index of loading equipment (%).
The technology used by the manufacturers of autonomous equipment uses presence sensors
and positioning projection, as well as analyzing the information of the embedded telemetry
system. Occurrences such as high braking rate, pressure variation between the suspensions
and high rolling resistance can cause speed reduction and even equipment shutdown.
For the implementation of a future project of autonomous equipment in mining, it is
necessary to avoid possible problems that the technology can cause. Unnecessary equipment
stops can occur in situations where the technology identifies risks to the equipment. The
main factors to be analyzed at the beginning of the project are:
• Width of loading locations;
• Construction, design and maintenance of access roads;
• Clovers and crosses;
• Impact of the presence of auxiliary equipment in the mine (tractors, motor graders,
vans);
• Drainage plan;
• Operational interventions (displacements, quality control, detonations, among others).
The work of adequacy of these mine parameters is fundamental to maximize the future
results of the implementation of an autonomous mine project.
5 RESULTS
The results of the simulations can be verified in Figures 4, 5 and 6.
Simulation 2 resulted in a 5% increase in the mass moved in the simulated period of one
day of operation and a 4% reduction in the effective use of transport equipment. Simulation
3 was 16% more productive, with a 6% increase in the actual use of the trucks.
The results related to the transport equipment can be verified in Figure 5. It can be analyzed that the indicator of queues in the load rises as the mass increases. This means that
there is an excess of trucks used in the operation and, therefore, there is a possibility of
reducing the transport fleet. This effect related to queues can also be verified in transport
productivity. The indicator increases 6% in simulation 2, but reduces 2% in comparison with
the autonomous scenario, influenced by the increase of the queues.
The use of the load equipment increases 17% with the implementation of the autonomous
equipment fleet. This result shows that the current operation, characterized by simulation 1,
has a larger dimensioning in loading and excessive equipment available. The loading productivity increased 25%, influenced by the 31% reduction in equipment idleness.
Table 3 shows the final result of the tests, with the comparison of the simulated scenarios.