The efficiency of the hauling process in the mines depends greatly on how effective the truck haulage system is. It is important to define precisely the three main constructs which include truck cycle time, cycle time variability, and haul road safety standards. The truck cycle time involves the amount of time that is needed for a truck to load, transport, dump and return (Ozdemir & Kumral, 2018). The cycle time variability is the variation in these times. Haul road safety standards include parameters like maximum grade, gradient and speed limit set to ensure the safety of the operators. One of the operational problems is reducing cycle time variability without affecting safety.
One of the aspects for having consistent haulage lies on maintaining the road to perfection through optimal design and regular maintenance. There are several factors that influence the safety of haul trucks. These include road gradient and rolling resistance. Poor conditions of these factors make the vehicles to be slow and put too much strain on the vehicle. With consistent maintenance of optimum rolling resistance, the trucks can operate at the optimum speed without the fear of skidding. Moreover, maintaining the road helps to minimize the delay that leads to cycle time variability thus making the operators feel less pressured to violate the safety protocol.
Cycle times have variability because of queuing at loading points. It has been noted that correct equipment matching such as ensuring that the loader matches the load on the trucks helps to minimize delays. If there are discrepancies in fleet matching, the trucks end up waiting for uncertain durations thus skewing the mean cycle times. Correct dispatch ensures that trucks get to the shovel once the shovel becomes available. Minimizing delays ensures that cycle times become standard and also that traffic moves in an efficient manner and hence becomes safer because traffic congestion causes accidents.
Modern mining involves the use of ICT in monitoring the truck movements in the mine. The safety management system of mines monitors the positions of vehicles. According to Baek and Choi (2019), using big data obtained from the ICT-based systems has helped engineers simulate trucks effectively. Using precise delay identification by dispatchers ensures that the traffic is moved away from traffic-congested or damaged areas of the roads. It therefore helps in maintaining stable cycle times without the need for drivers to travel faster than the required speed limits.
Predictive analytics, big data, and machine learning will help reduce operational variability. Stochastic models are used to predict production and to determine the probability distributions of truck travel times (Jung et al., 2021). Moreover, analytics of fleet operations help to find out optimal cycle times resulting in high payload while complying with safety requirements (George & Nojabaei, 2023). Intelligent algorithms make it possible to find the low-performing parts, and management is able to change the operation and follow all safety guidelines.
Finally, reduction in truck cycle time variation is not an option associated with neglecting the safety of the haul road. With effective maintenance of roads, proper matching of equipment, and application of big data and machine learning, mines can get rid of unexpected delays. Well-planned routes allow operators to maintain their speed and safe distance from each other. Thus, mines are able to reach a balanced state where productivity and safety work in favor of mining success.
References
Baek, J., & Choi, Y. (2019). Simulation of Truck Haulage Operations in an Underground Mine Using Big Data from an ICT-Based Mine Safety Management System. Applied Sciences, 9(13), 2639. https://doi.org/10.3390/app9132639
George, B., & Nojabaei, B. (2023). Data Analyses of Quarry Operations and Maintenance Schedules: A Production Optimization Study. Mining, 3(2), 347-366. https://doi.org/10.3390/mining3020021
Jung, D., Baek, J., & Choi, Y. (2021). Stochastic Predictions of Ore Production in an Underground Limestone Mine Using Different Probability Density Functions: A Comparative Study Using Big Data from ICT System. Applied Sciences, 11(9), 4301. https://doi.org/10.3390/app11094301
Ozdemir, B., & Kumral, M. (2018). A system-wide approach to minimize the operational cost of bench production in open-cast mining operations. International Journal of Coal Science & Technology, 6(1), 84-94. https://doi.org/10.1007/s40789-018-0234-1
