The loading process in truck and shovel operations is often modelled as a stochastic process due to the high variability (Soofastaei et al., 2018). An analysis of the haul truck payload data obtained from some mine sites around the world shows that the payload distribution can be estimated by a normal distribution (Soofastaei et al., 2014).
The variance associated with haul truck payloads is typically large and depends on some parameters such as particle size distribution, swell factor, material density, truck–shovel matching, the number of shovel passes and the bucket fill factor (Soofastaei et al., 2018).
Many attempts have been made to reduce the payload variance by using technologies such as on-board truck payload measurement systems, shovel payload management systems and fleet monitoring systems. Also, to load a truck in an effective manner, the shovel operator should load the truck within optimal payload limits using the minimum number of passes (Soofastaei et al., 2018).
The optimal payload can be defined in different ways, but it is always designed so that the haul truck will carry the greatest amount of material with lowest payload variance. The range of payload variance can be defined based on the capacity and power of truck. The payload variance in a surface mine fleet can significantly influence productivity due to truck congestion, or “bunching” phenomena, in large surface mines (Soofastaei et al., 2015).
The increasing of payload variance decreases the accuracy of a scheduled maintenance programme. This is because the rate of equipment wear is not predictable when the mine fleet faces a large payload variance. Minimising the variation of particle size distribution, swell factor, material density and fill factor can decrease the payload variance but it should be noted that it is not always possible to control all these parameters (Soofastaei et al., 2018).
Reference
Soofastaei, A., Aminossadati, S., Kizil, M., & Knights, P. (2014, December 1). Payload variance plays a critical role in the fuel consumption of mining haul trucks. https://www.semanticscholar.org/paper/Payload-variance-plays-a-critical-role-in-the-fuel-Soofastaei-Aminossadati/74be1e07fc47829b88540d22b8e2d6e6bd329e3e
Soofastaei, A., Aminossadati, S., Kizil, M., & Knights, P. (2015). Simulation of payload variance effects on truck bunching to minimise energy consumption and greenhouse gas emissions. https://www.semanticscholar.org/paper/Simulation-of-payload-variance-effects-on-truck-to-Soofastaei-Aminossadati/682c2b758cf12cb5958f36336431036366fb44ab
Soofastaei, A., Karimpour, E., Knights, P., & Kizil, M. (2018). Energy-Efficient Loading and Hauling Operations (K. Awuah-Offei, Ed.; pp. 121–146). Springer International Publishing. https://doi.org/10.1007/978-3-319-54199-0_7
