A shovel payload monitoring system is a technological structure that can be installed on excavators to allow for the changing estimation of the weight of the material within the dipper of the shovel prior to its dumping. Haul truck overloading can result in the excessive wear of mechanical components of the haul truck as well as pose safety risks to drivers. To calibrate such a system, adjustments to the algorithms and sensors of the shovel must happen to ensure that the estimated weights of the materials dumped from the shovel are equal to the physical weights of those materials. After calibration, validation of the system must be performed to ensure that it functions within an acceptable error margin in actual operational environments.
Precise payload measurement is key to modern mining operations. The productivity of a mining fleet is directly dependent upon the loading of the payloads onto the haul trucks within the best range of weights, neither overloading nor underloading. Overloading of the haul trucks leads to increased fuel consumption, tire failure and structural fatigue of the trucks. Underloading of the haul trucks results in inefficient use of mining equipment. A very accurate and well-calibrated shovel payload monitoring system enables mining operators to load the payloads onto the haul trucks at the best weight in real time, ensuring that the lifespan of the mining equipment in the fleet is extended.
Shovel payload estimation requires accurate mathematical modeling and parameter identification techniques. In this process, multiple parameters such as hoist motor torque, speed, and acceleration of structure are continuously collected through different kinds of sensors during the machine’s functioning period. To maintain the accuracy in payload mass translation, the system uses the technique of recursive parameter estimation, which allows computing parameters of mass, inertial, and viscous forces using the algorithm based on the recursive least squares approach (Sánchez et al., 2020). In this way, the system manages to adapt for any possible changes related to the machine’s degradation and drifting of sensors.
However, the process of calibration does not guarantee high accuracy of calculations. Thus, further system validation becomes necessary in order to ensure the continuous accuracy. For the purpose of validation, the shovel payload system estimates should be compared with the data collected with other highly precise measurement devices. For instance, one may use the volume scanning methods and physical scales in order to confirm the amount of payload carried in the final truck load (Knights & Reuter, 2023). As a result, the cumulative error in estimating dipper weights can be calculated.
The use of a payload system validates issues that relate to operational sustainability and efficiency in terms of cost reduction. The reduction of variance associated with the system reduces inefficiencies like trucks being bunched up in haul roads because of being overloaded and slow. In addition, the use of strict payload systems will help reduce some logistical issues making it highly sustainable and reducing the carbon footprints of heavy vehicle activities (Harris et al., 2024). Haul roads will deteriorate more slowly and the cost of maintenance will be lower due to the predictability of the mining process.
At the end of the day, the calibration and validation of payload monitoring systems is key in ensuring maximum efficiency of fleet operations. Giving the operators accurate information on how to load the loads helps mining companies reach the required payloads thus narrowing down the difference between capacity and operation. It removes guesswork and the stress of loading and reduces the load on the trucks as well. With operations looking for technological ways to become efficient, calibrated monitoring framework will be part of that process.
References
Harris, I., Bermudez Bermejo, D. E., Crowther, T., & McDonald, J. (2024). Factors Affecting Truck Payload in Recycling Operations: Towards Sustainable Solutions. Logistics, 8(4), 118. https://doi.org/10.3390/logistics8040118
Knights, P., & Reuter, M. (2023). Economic benefits of load volume scanning of underground mining trucks. CIM Journal, 14, 185–191. https://doi.org/10.1080/19236026.2023.2186642
Sánchez, M. C., Torres-Torriti, M., & Cheein, F. A. (2020). Online Inertial Parameter Estimation for Robotic Loaders. IFAC-PapersOnLine, 53, 8763–8770. https://doi.org/10.1016/j.ifacol.2020.12.1373
