Enabling on-site mechanical analysis of drill cores for sustainable and efficient mining

Projektinformationen

LASO-LIBS

ID Finanzhilfevereinbarung: 101218479

DOI

10.3030/101218479

EK-Unterschriftsdatum 30 April 2025

Startdatum 1 Mai 2025

Enddatum 30 April 2027

Finanziert unter

• The European Innovation Council (EIC)

Gesamtkosten

Keine Daten

EU-Beitrag

€ 2 495 500,00

Koordiniert durch

Lumo Analytics Oy
Finland

Periodic Reporting for period 1 - LASO-LIBS (Enabling on-site mechanical analysis of drill cores for sustainable and efficient mining)

Berichtszeitraum: 2025-05-01 bis 2026-04-30

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

Mining companies face growing challenges related to efficiency, sustainability, and resource utilization. With increasing demand for metals driven by global megatrends such as electric transportation, renewable energy, and geopolitical pressures, there is an urgent need for advanced technologies that optimize mining operations. We are at the forefront of this transformation with our innovative LASO-LIBS technology, which provides instant, on-site analysis of mechanical, elemental, and mineral properties of ore bodies. Currently, the mining industry relies heavily on laboratory-based mechanical tests such as the Bond Ball Mill test, resulting in 2-3 month waiting period. Our solution enables instant on-site mechanical analysis, allowing for faster, more accurate decision-making in mining operations. By leveraging proprietary algorithms that analyze optical and acoustic signals generated through laser ablation, our technology provides precise estimations of rock's mechanical properties, as well as elemental and mineral compositions, directly in the field. This technological breakthrough addresses a critical gap in the industry, as mining companies typically lack adequate tools to estimate mechanical properties on-site. As a result, operations often suffer from inefficient use of electricity, excessive chemical consumption, and the generation of significant waste. Our solution empowers mining companies to optimize operations, reducing energy consumption and environmental impact while improving productivity. Our solution aligns with the European Union's objectives to increase self-sufficiency in critical mineral production and meet the demands of the green transition. With on-site mechanical analysis now a reality, we are leading the way toward a more sustainable, efficient, and responsible future in the mining industry.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

The project focused on the development of the LASO-LIBS system as an integrated solution for real-time analysis of mechanical, elemental, and mineral properties of drill cores in operational mining environments.

Significant progress has been achieved across hardware, software, and data infrastructure. The next-generation scanning device has been designed, incorporating major improvements in mechanical robustness, measurement stability, and automation. A container-based field deployment concept has been implemented and validated, enabling reliable operation under real field conditions. Global connectivity has been demonstrated through the use of satellite communication (Starlink), allowing real-time data transfer and remote system access in remote locations.

On the analytical side, substantial advancements have been made in data processing and modelling. Machine learning methods have been developed for quantitative elemental analysis, achieving high accuracy and enabling detection of trace and minor elements. New capabilities have been introduced, including determination of the origin of carbon and detection of halogens such as fluorine using molecular emissions. Mineral classification has been significantly improved through hierarchical modelling approaches, and a multimineral unmixing method has been developed to better represent heterogeneous samples.

In parallel, a novel approach for estimating mechanical properties based on acoustic signals generated during laser ablation has been implemented. The required hardware and data acquisition systems have been successfully integrated and validated, and initial datasets have been collected for further model development.

A scalable data platform has been developed to support large-scale data processing, machine learning workflows, and system monitoring. The developed solutions have been validated using customer data and pilot deployments, demonstrating their applicability in real operational environments.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

The LASO-LIBS system represents a significant advancement beyond the current state of the art in mining analytics by enabling real-time, multi-parameter analysis directly in the field.

Traditionally, elemental, mineralogical, and mechanical analyses are performed separately using laboratory-based methods, often resulting in long turnaround times. The developed solution integrates these capabilities into a single platform, providing immediate insights into ore properties. In particular, the ability to estimate mechanical properties from laser-induced acoustic signals represents a novel approach not currently available in standard industry workflows.

The integration of machine learning-based elemental quantification, molecular LIBS analysis, and multimineral unmixing further extends the analytical capabilities beyond conventional LIBS systems. These developments enable more accurate modelling of lithology, alteration, and mineralization, supporting improved exploration and beneficiation decisions.

The project also introduces a field-deployable, scalable system architecture, including remote connectivity and data platform integration, which supports deployment in remote and challenging environments.

To ensure further uptake and success, key next steps include:

- Expansion of labelled datasets for mechanical property modelling
- Continued validation in operational environments
- Scaling of manufacturing and deployment capabilities
- Integration with customer data systems and workflows
- These steps will support commercialization and wider adoption of the technology in the mining industry.