Circular Economy Facilitator for Mining Industry (CEFMI)

This following shares key insights from the Circular Economy Facilitator for the Mining Industry project, which explored new ways to extend the life of high-value mining equipment. The focus is on two important machines in Swedish mining: the KOMATSU 2350 wheel loader at Boliden and the LH621i loader at LKAB.

While traditional strategies often rely on general estimates or financial metrics like life cycle cost (LCC) and return on investment (ROI), this project takes things further. By using detailed operational and maintenance data—down to the component and subsystem level—it aims to create smarter, more sustainable life extension plans that align with circular economy goals.

Key Findings and Developed Tools

1. Component-Level Degradation is Critical to Overall Asset Health
   Our analysis reveals that high-value mining equipment consists of numerous components with varying degradation patterns:
   • The KOMATSU 2350 comprises 8 systems, 60 subsystems, and 675 components
   • Critical end-of-life determinants include planetary gears and frame cracks
   • Component-specific monitoring is essential for accurate life extension decisions
   
   
2. Predictive Indicators by Equipment Type
   For the KOMATSU 2350:
   • Oil analysis (particularly iron content) provides early warning signs of component degradation
   • Vibration measurements from accelerometers installed on critical components offer real-time degradation monitoring
   
   For the LH621i:
   • Frame cracks and welding repair frequency are primary indicators of end-of-life
   • Front and rear axle conditions significantly impact equipment longevity
   
   
3. Maintenance Costs Follow Predictable Patterns
   Our analysis of historical maintenance data reveals:
   • Corrective maintenance costs typically follow an accelerating growth pattern over time
   • Preventive maintenance costs show more moderate and predictable growth patterns
   • Machine unavailability increases linearly at approximately xxx hours per month over a 10-year projection period
   
   
4. Life Extension Actions Provide Significant Value
   The study demonstrates that:
   • Strategic life extension actions can significantly extend equipment useful life
   • Timing of interventions is crucial for maximizing return on investment
   • Multiple smaller interventions may be more effective than single major overhauls
   
   
5. Hybrid Modeling Approaches and Custom Software Tool Improve Decision-Making
   We developed progressively sophisticated models for predicting remaining useful life:
   • Basic models provide reasonable estimations with minimal data requirements
   • Advanced models incorporating multiple variables offer increased prediction accuracy
   
   Additionally, as part of the CEFMI project, we developed a custom software application using Python (Figure 1) that:
   • Provides a user-friendly dashboard for monitoring mining equipment health
   • Visualizes critical maintenance parameters including vibration data and oil analysis
   • Implements the RUL prediction models for real-time equipment lifespan estimation
   • Offers cost analysis tools to support maintenance and replacement decisions
   • Enables maintenance personnel to track equipment degradation over time

Recommendations

Based on our findings, we recommend the following strategies for optimizing life extension decisions:

1. Implement Comprehensive Condition Monitoring Systems
   • Install vibration sensors on critical components (planetary gears)
   • Conduct regular oil analysis to track contamination levels
   • Integrate all monitoring data into a centralized system for holistic assessment
   
   
2. Adopt Component-Level Maintenance Strategies
   • Develop customized maintenance schedules for different subsystems based on their specific degradation patterns
   • Prioritize critical components that determine end-of-life (planetary gears, frames, axles)
   • Implement condition-based maintenance approaches that utilize real-time monitoring data
   
   
3. Optimize Timing of Life Extension Interventions
   • Use predictive models to determine optimal timing for life extension actions
   • Consider multiple smaller interventions rather than single major overhauls
   • Balance immediate repair costs against long-term benefits and production losses
   
   
4. Leverage Data Analytics and Custom Software Tools for Decision Support
   • Utilize hybrid modeling approaches (combining physical models with data-driven techniques)
   • Implement neural networks for improved RUL predictions
   • Continuously refine models with new operational and maintenance data
   • Deploy the custom-developed CEFMI Tkinter application for real-time monitoring and decision support
   • Integrate predictive models into user-friendly software interfaces for maintenance staff
   
   
5. Integrate Economic, Technical, and Sustainability Factors in Decision-Making
   • Consider maintenance costs, unavailability impacts, and sustainability metrics when making life extension decisions
   • Track system-specific costs to identify high-impact areas for intervention
   • Balance capital expenses (new equipment) against operational expenses (maintenance)

Expected Benefits

Implementation of these project recommendations is expected to yield:
• Extended useful life of high-value mining equipment, supporting Sweden's circular economy goals
• Reduced total cost of ownership through optimized maintenance strategies
• Improved operational reliability and reduced unplanned downtime for Swedish mining operations
• More accurate capital planning for equipment replacement
• Enhanced return on investment for life extension actions
• Decreased environmental footprint through resource efficiency and reduced manufacturing demand
• Strengthened competitive position of the Swedish mining industry through cost optimization
• Knowledge transfer and capacity building across the Swedish mining sector (BOLIDEN and LKAB)

Conclusion

The Circular Economy Facilitator for Mining Industry project demonstrates that a component-level approach to life extension, coupled with advanced monitoring and predictive modeling, provides significant advantages over traditional end-of-life decision-making methods. By implementing the recommended strategies, Swedish mining operations can optimize the longevity of their high-value equipment while maintaining operational efficiency and cost-effectiveness.

The customized life extension strategies developed for both the KOMATSU 2350 and LH621i showcase how targeted interventions based on comprehensive data analysis can transform maintenance approaches from reactive to proactive, ultimately delivering substantial operational and financial benefits. These approaches align perfectly with Sweden's national circular economy objectives by extending asset lifecycles, reducing resource consumption, and minimizing waste generation in the mining industry.
This project represents an important step toward sustainable mining practices in Sweden and provides a model that can be expanded to other equipment types and mining operations throughout the Nordic region.

The custom Tkinter application developed as part of this project serves as a practical implementation of the research findings, bridging the gap between advanced predictive models and day-to-day maintenance operations. This software tool exemplifies how digital technologies can support circular economy principles in the mining sector by facilitating more informed decisions about equipment life extension.

Acknowledgements

With support from the Strategic innovation programme for process industrial IT and automation (PiiA), a joint effort by Vinnova, Formas and the Swedish Energy Agency.

Partners: Luleå University of Technology, Boliden Mineral AB, Filterteknik AB, Kaunis Iron AB, LKAB, Sandvik AB, SPM Instrument AB and Trafikverket.