489
delays were inversely proportional to shearer production rate since the shearer production
rate decreased when non-operational time of the shearer was increased, as expected. When
the shearer delays was decreased to 80% of its actual values, shearer production rate in a shift
was increased by 22.34%. On the contrary, and increase of 20% of its actual values led to a
15.23% decrease of shearer production rate in a shift. On the other hand, reversal time of the
shearer drum had almost no effect on shearer production rate. Two main reasons that led to
this situation are that the shearer having very short drum reversal times and there were few
drum reversal operations in a shift.
5 CONCLUSION
The mining industry relies on complex systems with numerous variables that affect the
production in the system. Within the scope of the study, a model that represented a fullymechanized LTCC system was developed and the shearer performance was investigated in
one production panel of a real operation located in Soma, Turkey. According to the sensitivity analysis performed on the shear production rates, time between arrivals of the shearer
delays were the most effective variable for the developed simulation models. This conclusion
is based on the change of shearer production with respect to the variation in the operational
duration of the shearer. In other words, shearer operation was interrupted continuously in
the mine affecting the production rate. Conveyor systems used for the transportation of coal
in the mine are subject to “operational delays” that can be related to breakdowns that occur
in any section of the system and cause an interruption of the whole operation. The main aim
of engineers on site is to increase the system efficiency and in order to achieve better system
efficiency, operational delays have to be decreased. Operational delays can be minimized by
utilizing operational data and making it part of the decision making process in daily mine
management. The data collected for this study were limited and could be increased by creating an easy-to-access environment for engineers. This way, operational data of the LTCC
operation would be utilize to track the performance of the equipment in real time. Additionally, training of the operators (decrease in cleaning operation in the mine) and following a
pre-determined maintenance strategy for the machines in the operations (decrease in breakdowns of the machines) are crucial for an efficient LTCC operation.
Future studies are planned to compare bi-directional cutting system with the uni- directional
cutting system on the efficiency consideration of the mine and integrating the available data
on-site for further analysis of the operation.
REFERENCES
Azimi, Y. & Osanloo, M. 2011. Determination of open pit mining cut-off grade strategy using combination of nonlinear programming and genetic algorithm. Archives of Mining Sciences 56(2): 189–212.
Banks, J., Carson, J., Nelson, B.L. & Nicol, D. 2010. Discrete-Event System Simulation. NJ: Prentice Hall.
Darling, P. (ed) 2011. SME Mining Engineering Handbook. SME.
Hustrulid, W.A. & Bullock, R.C. (eds) 2001. Underground Mining Methods: Engineering Fundamentals
and International Case Studies. SME.
İnci, U. 2002. Depositional evolution of Miocene coal successions in the Soma coalfield, western Turkey. International Journal of Coal Geology 51(1): 1–29.
Karayiğit, A.İ., Littke, R., Querol, X., Jones, T., Oskay, R.G. & Christanis, K. 2017. The Miocene coal
seams in the Soma Basin (W. Turkey): Insights from coal petrography, mineralogy and geochemistry. International Journal of Coal Geology 173: 110–128.
Lamghari, A., Dimitrakopoulos, R. & Ferland, J.A. 2015. A hybrid method based on linear programming and variable neighborhood descent for scheduling production in open-pit mines. Journal of
Global Optimization 63(3): 555–582.
Le, T.D., Mitra, R., Oh, J., & Hebblewhite, B. 2017. A review of cavability evaluation in longwall top
coal caving. International Journal of Mining Science and Technology 27(6): 907–915.
delays were inversely proportional to shearer production rate since the shearer production
rate decreased when non-operational time of the shearer was increased, as expected. When
the shearer delays was decreased to 80% of its actual values, shearer production rate in a shift
was increased by 22.34%. On the contrary, and increase of 20% of its actual values led to a
15.23% decrease of shearer production rate in a shift. On the other hand, reversal time of the
shearer drum had almost no effect on shearer production rate. Two main reasons that led to
this situation are that the shearer having very short drum reversal times and there were few
drum reversal operations in a shift.
5 CONCLUSION
The mining industry relies on complex systems with numerous variables that affect the
production in the system. Within the scope of the study, a model that represented a fullymechanized LTCC system was developed and the shearer performance was investigated in
one production panel of a real operation located in Soma, Turkey. According to the sensitivity analysis performed on the shear production rates, time between arrivals of the shearer
delays were the most effective variable for the developed simulation models. This conclusion
is based on the change of shearer production with respect to the variation in the operational
duration of the shearer. In other words, shearer operation was interrupted continuously in
the mine affecting the production rate. Conveyor systems used for the transportation of coal
in the mine are subject to “operational delays” that can be related to breakdowns that occur
in any section of the system and cause an interruption of the whole operation. The main aim
of engineers on site is to increase the system efficiency and in order to achieve better system
efficiency, operational delays have to be decreased. Operational delays can be minimized by
utilizing operational data and making it part of the decision making process in daily mine
management. The data collected for this study were limited and could be increased by creating an easy-to-access environment for engineers. This way, operational data of the LTCC
operation would be utilize to track the performance of the equipment in real time. Additionally, training of the operators (decrease in cleaning operation in the mine) and following a
pre-determined maintenance strategy for the machines in the operations (decrease in breakdowns of the machines) are crucial for an efficient LTCC operation.
Future studies are planned to compare bi-directional cutting system with the uni- directional
cutting system on the efficiency consideration of the mine and integrating the available data
on-site for further analysis of the operation.
REFERENCES
Azimi, Y. & Osanloo, M. 2011. Determination of open pit mining cut-off grade strategy using combination of nonlinear programming and genetic algorithm. Archives of Mining Sciences 56(2): 189–212.
Banks, J., Carson, J., Nelson, B.L. & Nicol, D. 2010. Discrete-Event System Simulation. NJ: Prentice Hall.
Darling, P. (ed) 2011. SME Mining Engineering Handbook. SME.
Hustrulid, W.A. & Bullock, R.C. (eds) 2001. Underground Mining Methods: Engineering Fundamentals
and International Case Studies. SME.
İnci, U. 2002. Depositional evolution of Miocene coal successions in the Soma coalfield, western Turkey. International Journal of Coal Geology 51(1): 1–29.
Karayiğit, A.İ., Littke, R., Querol, X., Jones, T., Oskay, R.G. & Christanis, K. 2017. The Miocene coal
seams in the Soma Basin (W. Turkey): Insights from coal petrography, mineralogy and geochemistry. International Journal of Coal Geology 173: 110–128.
Lamghari, A., Dimitrakopoulos, R. & Ferland, J.A. 2015. A hybrid method based on linear programming and variable neighborhood descent for scheduling production in open-pit mines. Journal of
Global Optimization 63(3): 555–582.
Le, T.D., Mitra, R., Oh, J., & Hebblewhite, B. 2017. A review of cavability evaluation in longwall top
coal caving. International Journal of Mining Science and Technology 27(6): 907–915.