Imagine trying to find tiny gold flakes in a mountain of sand; it's slow and wasteful. This project builds a smart 'digital eye' that scans rocks in real-time to see exactly where the valuable minerals are. By using AI to guide the machinery, mines can stop wasting energy on useless stone and only process the good stuff.
Mining companies struggle with the high cost and inefficiency of extracting critical raw materials from low-grade deposits, leading to high energy consumption and dependency on external supplies.
A smart modular solids analysis system featuring online mineral liberation analysis and AI/ML-driven control loops for mining sites.
If you are a mining company dealing with low-grade deposits that are too expensive to process — this project developed a smart modular solids analysis system that increases financial viability by lowering extraction costs.
If you are a plant operator dealing with inefficient energy use and high gas emissions — this project developed AI and ML-based control loops that optimize the processing stages to reduce waste.
If you are a recovery firm dealing with the challenge of extracting secondary raw materials from old mining waste — this project developed online mineral liberation analysis to make recycling more efficient.
It uses a smart modular solids analysis system and AI techniques to create advanced control loops, which makes the extraction of low-grade deposits profitable.
The project aims to demonstrate extraction and processing technologies specifically for integration on mining sites, though specific scale metrics are not yet listed.
Based on available project data, the consortium is developing a business case and exploitation strategy to manage the commercial rollout.
The technology is designed to align with the EU CRM Act and policies regarding green and digital objectives to reduce EU dependency on raw materials.
It is designed as a modular solids analysis system that enables online mineral liberation analysis and data-driven operations.
The consortium is heavily industry-weighted with a 56% industry ratio, including 9 industrial partners and 4 SMEs. This strong commercial presence, combined with a multidisciplinary group of 16 partners across 6 countries (including resource-rich Brazil, Canada, and Ukraine), indicates a high focus on market viability and practical application rather than pure academic research.
Contact Universitat Politècnica de Catalunya regarding the BLOOM project
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