Modeling the influence of electric shovel operator performance on mine productivity

ABSTRACT

Truck-shovel systems are commonly used for material handling during surface mining. Not only does the overall outcome of a mine rely heavily on haulage system performance, but it constitutes a significant portion of mine operational costs. Using detailed data from a shovel monitoring system, this study statistically analyzes variations among key performance activities by shovel operators. Based on the results, a novel operator relative score system is introduced. To quantify the extent to which different aspects of a mining operation could be influenced by shovel operator practices, an operator discrete event simulation sub-module was developed and verified. Results showed that operators could affect mine production, number of trucks, and queue times by up to 20, 16, and 41%, respectively. This simulation model can be used by mining companies to assess their current shovel performance and improve production by modifying shovel operator practices.

RÉSUMÉ

Les systèmes de pelle de camion sont souvent utilisés pour la manutention des matériaux pendant l’exploitation minière de surface. Non seulement le résultat global d’une mine dépend largement du rendement du réseau de transport, mais aussi il représente une part importante des coûts d’exploitation de la mine. À l’aide de données détaillées provenant d’un système de surveillance des pelles, cette étude analyse statistiquement les variations entre les principales activités de rendement des opérateurs de pelles. Sur la base des résultats, un nouveau système de score relatif de l’opérateur est introduit. Afin de quantifier la mesure dans laquelle différents aspects d’une exploitation minière pourraient être influencés par les pratiques de l’opérateur de pelle, un sous-module de simulation d’événements discrets de l’opérateur a été élaboré et vérifié. Les résultats ont montré que les exploitants pouvaient avoir une incidence sur la production minière, le nombre de camions et les temps de file d’attente de 20, 16 et 41 %, respectivement. Ce modèle de simulation peut être utilisé par les sociétés minières pour évaluer leur rendement actuel et améliorer la production en modifiant les pratiques de l’opérateur de pelle.

KEYWORDS:

• Electric rope shovel (RS)
• Human factor
• Performance monitoring
• Simulation
• Surface mining

MOTS CLÉS:

• exploitation à ciel ouvert
• facteur humain
• pelle électrique à câble
• simulation
• surveillance du rendement

Acknowledgments

The authors would to thank CIM peer reviewers and the editorial team and especially Ms. Janice M. Burke for their great contributions.

Additional information

Notes on contributors

A. Yaghini

A. Yaghini is a PhD student in the School of Mining and Petroleum Engineering at the University of Alberta. His specific interest is in optimum deployment of advanced technologies in the mining industry by addressing the important role of human factors. His current research involves digital innovations and automated mining.[email protected]

R. A. Hall

Dr. R. A. Hall is a professor in the Department of Civil & Environmental Engineering School of Mining and Petroleum Engineering at the University of Alberta. Prior to this role, he spent 15 years at The University of British Columbia. His areas of research include mining equipment design and automation, equipment maintenance and reliability, comminution, and energy reduction.

D. Apel

Dr. D. Apel is a professor in the Department of Civil & Environmental Engineering School of Mining and Petroleum Engineering at the University of Alberta. His research areas concentrate around geomechanical mine design for underground and surface mines and applied geophysics. Before joining the University of Alberta, Dr. Apel worked at the Missouri University of Science and Technology. His industrial experience comes from working for underground coal and uranium mines in Poland and Canada.