Shovel Performance

Keeping costs down and productivity high is a constant challenge for surface/open-pit mine operators. With truck haulage being the single biggest operating cost (up to 60%) in most mines, it makes sense to intensively monitor and manage this activity. Carefully controlling payload is vital for making sure trucks are not under or overloaded and to ensure that the maximum amount of material is moved without causing excessive fuel use or premature wear to braking systems and other components.

Traditionally, payload monitoring has been performed by truck-based systems. However, these systems have a few key limitations– for example, the need for regular recalibration of truck sensors, some trucks not having an on-board weighing system at all, and delays in payload data reaching the (shovel) operator who can actually influence the loading process. These factors have a major, negative impact on the accuracy and usefulness of the data produced.

Fortunately, there is a better way to optimise payload and better manage the truck/shovel interaction: monitor payload at the source by using a shovel-based payload monitoring system.

Monitoring the payload at the source produces very accurate data, optimises shovel performance, and ensures accurate and consistent truck loading – leading to increased productivity and reducing operating costs.

Argus, from MineWare, is a superior shovel payload monitoring system providing more than 100 global mining operations with actionable information in real-time, allowing them to boost productivity, improve shovel performance, and lower costs, without compromising safety.

Implementing the Argus shovel monitoring system at your surface mine will provide several benefits, including:

  • Productivity improvement through shovel optimisation of up to 16% — guaranteed
  • Eliminate the need for frequent calibrations of truck (strut) sensors
  • Provide real-time feedback to the shovel operator so he can adjust as he works, ensuring efficiency and optimising shovel performance
  • Average truck load improvement through the elimination of under and overloading, leading to improved productivity
  • Reduce operator performance variations by identifying the characteristics of the best operators, and then training and managing accordingly