An assessment of the underlying causes for the difference between theoretical and real-world production rates of mining shovels

The goal of a mining operation is to extract the maximum value from exploiting the orebody. The equipment used by these mining operations has a nominally rated performance to achieve needed annual production. However, this nominally rated performance is not achieved during operations. This study assesses the underlying causes of reduced shovel production in a mining operation. The following research questions will be answered in this study: What factors contribute to deviations from the predicted performance? What factors are the most significant? What is the effect of automated trucks on shovel productivity? Can a data-driven model be developed to predict the actual productivity of the shovel in the real world? The underlying causes of reduced shovel production are assessed through a literature review and five case studies. The case studies consisted of three data analytical desk studies, interviews with industry professionals, and a mine visit to north Africa. The literature review shows that the mining industry uses formulas to determine the theoretical production rates. The factors in the shovel production formula should be predicted with frequency distributions. Also, truck automation will increase trucking hours, impacting shovel productivity through truck exchange time. The first case study shows a sensitivity analysis of the shovel formula. This sensitivity analysis shows that the swell factor, density, bucket fill, efficiency, and cycle time have a more significant impact than truck exchange time, dumping of the first bucket, and the number of cycles. The second case study shows the underperformance of different electric rope shovel models in different mining operations. The third case study compares the theoretical shovel capacity with the total material moved for 16 years. This shovel capacity and total material ratio should be between 1-2.5 for a mining operation to be classified as above-average-in-class. The fourth case study shows the different factors influencing shovel productivity based on the interviewees’ responses. The last case study shows the effects of operational decisions on a mining operation. The literature review and case studies are used to develop a flowchart regarding the factors influencing shovel productivity. This flowchart was used to synthesise the results. The difference between theoretical and real-world production rates can be decreased when the frequency distributions are known for all the shovel production formula factors. This shovel production formula for annual production rate can be divided into three main pillars. These three pillars are the hourly production rate, use of availability, and mechanical availability. The pillars allow OEMs or mining companies to implement the correct improvement measures to improve shovel productivity. However, one solution for every mining operation will be impossible due to the uncertainty in the data and variability of each mining operation. To conclude, the factors that contribute to deviations from the predicted performance are categorised as uncontrollable (weather and geographical location), direct (density and cycle time), and indirect (fragmentation and face dimensions), which all impact shovel productivity. The most significant factors are not found during the study, but solving underperformance in the direct factors will solve most of the problems. The effect of automated trucks on shovel productivity will result in additional trucking capacity, which will need to be absorbed by the shovel. Lastly, a data-driven model can be developed with access to all the data from a mining operation. However, this data is often not available to an OEM. Therefore, it is not advisable to develop such a model