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Towards the Application of Process Mining in the Mining Industry—An LHD Maintenance Process Optimization Case Study

Author

Listed:
  • Nicolas Velasquez

    (Department of Mining Engineering, Pontificia Universidad Católica de Chile, Santiago 8320000, Chile)

  • Angelina Anani

    (Department of Mining and Geological Engineering, The University of Arizona, Tucson, AZ 85719, USA)

  • Jorge Munoz-Gama

    (Department of Computer Science, Pontificia Universidad Católica de Chile, Santiago 8320000, Chile)

  • Rodrigo Pascual

    (Centre for Advanced Asset Analytics, Mechanical Engineering, Universidad de Chile, Santiago 8320000, Chile)

Abstract

Inefficiencies in mine equipment maintenance processes result in high operation costs and reduce mine sustainability. However, current methods for process optimization are limited due to a lack of access to structured data. This research aims to test the hypothesis that process mining techniques can be used to optimize workflow for mine equipment maintenance processes using low-level data. This is achieved through a process-oriented analysis where low-level data are processed as an event log and used as input for a developed process model. We present a Discrete-Event Simulation of the maintenance process to generate an event log from low-level data and analyze the process with process mining. A case study of the maintenance process in an underground block caving mine is used to gain operational insight. The diagnosis of the mine’s maintenance process showed a loss of 23,800 equipment operating hours per year, with a non-production cost of about 1.12 MUSD/year. Process mining obtained a non-biased representation of the maintenance process and aided in identifying bottlenecks and inefficiencies in the equipment maintenance processes.

Suggested Citation

  • Nicolas Velasquez & Angelina Anani & Jorge Munoz-Gama & Rodrigo Pascual, 2023. "Towards the Application of Process Mining in the Mining Industry—An LHD Maintenance Process Optimization Case Study," Sustainability, MDPI, vol. 15(10), pages 1-18, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:7974-:d:1146271
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    References listed on IDEAS

    as
    1. Marcin Szpyrka & Edyta Brzychczy & Aneta Napieraj & Jacek Korski & Grzegorz J. Nalepa, 2020. "Conformance Checking of a Longwall Shearer Operation Based on Low-Level Events," Energies, MDPI, vol. 13(24), pages 1-18, December.
    2. Park, Jinkyun & Jung, Jae-Yoon & Heo, Gyunyoung & Kim, Yochan & Kim, Jaewhan & Cho, Jaehyun, 2018. "Application of a process mining technique to identifying information navigation characteristics of human operators working in a digital main control room – feasibility study," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 38-50.
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    Cited by:

    1. Leoni, Leonardo & De Carlo, Filippo & Tucci, Mario, 2023. "Developing a framework for generating production-dependent failure rate through discrete-event simulation," International Journal of Production Economics, Elsevier, vol. 266(C).

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