In mining, 5-10% of the material excavated and transported is waste material or so-called gangue. This % might seem low but mining dilution is one of the most important factors affecting the economy of mining projects. There are four main state-of-the-art approaches to analyzing ore composition but all of them take place at the lab and none in real time at the conveyor belt. ROCS will help make mining financially and environmentally sustainable by allowing mining companies to understand the ore composition right at the start of the production phase. A LIBS sensor is added to the conveyor belt in order to optimize production through modern mining and processing techniques. This technology can be used to detect all kinds of minerals and metals including critical raw materials, featuring better signal-to-noise-ratios than leading competitors. The raw materials sector is crucial to Europe’s economy. With the increasing population, securing reliable and unhindered access to certain raw materials is a growing concern within the EU. However, the EU is strongly dependent on imports as mining is becoming increasingly unsustainable. The global mining industry produces around €3 trillion of minerals (excluding diamonds). The ROCS system will be a product for innovative and sustainable mines. Assuming that ROCS can improve the ore pre-concentration process in order to detect the dilution and decrease the gangue percentage by 4 points (from 14-17% to 10-12%), the mine can save over 30% of the energy required for transportation, crushing and grinding these rocks. This translates into 25,000 to 30,000 MWh annually, i.e. €2.5-3M of costs and 22,500-27,000M tons of CO2, not including other savings (water, chemicals, man-hours). SPECTRAL is a high-tech start-up from Delft. We have a fruitful relation to TNO that has a strong pedigree in designing and building spectroscopic instruments for applications including medical diagnostics, semiconductors and space.

Although raw materials are of strategic importance for the European economy in key exponentially growing value chains such as the energy transition where Europe should strengthen its resilience, independence, and competitiveness, European mining operators fail to meet the demand, as they suffer from low productivity, poor waste management, resource-intensity and the consequent environmental impacts. To meet the ever-increasing demand and negative market prospects, while mitigating the environmental and societal impacts, the mining industry needs a drastic transformation. In this context, DINAMINE aims to demonstrate a holistic mine management approach, based on (i) artificial intelligence-based data analytics tools for real-time mine-to-port monitoring of the risks, performance, environmental footprint, maintenance needs, product quality and recovery rates, (ii) machine automation and robotization strategies to enhance safety and productivity, (iii) on-site studies to identify best practices for carbon neutral logistics/transportation, more energy-efficient processing, and waste/tailings handling and valorisation. Our goal is to propose tools that enable a smarter, more efficient, more selective, safer and more sustainable mining industry, with a specific focus on European SMEs. Technologies developed will be upscaled and tested, and studies performed in 2 small-medium scale European mining operations: one open-pit feldspar mine (Portugal), and one underground graphite mine (Norway) and their associated processing plants. The project is brought forward by a high-level industry-led multidisciplinary consortium of 11 partners from 6 European countries, including 3 research institutes, and 6 industries (including 4 SMEs). Via increased selectivity and productivity, DINAMINE will bring several impacts for EU and global mines, helping them to improve their IRR and ultimately contribute to an increase in EU raw materials security of supply.

The continuous effort and increased demand of the raw materials are directing the mining companies to excavate minerals at greater depths. This trend is challenging the current mining operations and the existing traditional technologies towards the objective to retain profitability, while achieving the latest Green Deal environmental vision and securing human workers safety. A key enabler, to address these challenges and to foster a sustainable development of the mining industry, is the development and deployment of innovative technologies for resource efficient extraction of the EU’s raw materials, as well as near mine exploration of critical raw materials in currently non-extracted ore bodies in existing or abandoned ones. PERSEPHONE is aiming to address these challenges by developing of the pioneering technologies for pushing the limits of EU mining industry and embodiment of autonomous and integrated near mine exploration capability to access deep deposits of critical raw materials through hard-to-reach deep and abandoned mines. The overall concept and vision of PERSEPHONE will be achieved by reducing the size of mining machines currently adapted to the human scale and embedding autonomy for risk-aware navigation and full digitalization of the extraction process by digital twin creation and key enabling technologies validation at TRL 5. Additionally, PERSEPHONE is introducing completely novel approaches in online near mine exploration core analysis and overall integration of related data analytics to the mine expansion. Thus, PERSEPHONE allows to foster green transition by reducing the cost and waste generated from deep-mining operations and foster the vision of zero human presence in highly hazardous areas. These will allow to achieve PERSEPHONE’s overall goal to digitalize and automate extraction value chain by creation of new concepts of energy-efficient autonomous drilling machines with advanced perception capabilities for navigation, face drilling, and core extraction, which will enable data-driven digital twin creation and geological modelling for further enhanced decision support and optimal extraction planning.

In context of the transition to low-carbon, green and sustainable steel production in Europe, disruptive technologies to reduce the environmental footprint as close to zero as possible, seamless digitalisation of production processes, and skilled people to co-design and understand the transformation process are necessary. DiGreeS will address these needs by implementing an integrated digitalisation approach across the steel value chain, enabling better use of process data collected and ensuring the involvement of human experience for easier industrial integration. The aim of DiGreeS is to develop a user-friendly digital platform for networked production based on novel and soft sensors and related approaches and models to support efficient feedstock verification and real-time control of electric arc furnace crude steel production, increasing process yield while improving the quality of intermediate and final steel products. In this context, the potential of artificial intelligence techniques will be fully exploited to support the optimal use of industrial data, and different scenarios specific to each use case will be modelled. The digital platform will be implemented and verified in industrial process lines of the three use cases: scrap/secondary raw material verification, optimisation of the electric arc furnace processes and optimisation of the levelling of steel sheets. DiGreeS aims to improve the quality of crude steel and finished products, optimize scrap usage, and improve energy efficiency in the steel production process. DiGreeS has the potential to save up to €800 million in costs annually and reduce CO2 emissions from the steelmaking industry by up to 6 million tonnes per year.

The overall aim of Real-Time-Mining is to develop a real-time framework to decrease environmental impact and increase resource efficiency in the European raw material extraction industry. The key concept of the proposed research promotes the change in paradigm from discontinuous intermittent process monitoring to a continuous process and quality management system in highly selective mining operations. Real-Time Mining will develop a real-time process-feedback control loop linking online data acquired during extraction at the mining face rapidly with an sequentially up-datable resource model associated with real-time optimization of long-term planning, short-term sequencing and production control decisions. The project will include research and demonstration activities integrating automated sensor based material characterization, online machine performance measurements, underground navigation and positioning, underground mining system simulation and optimization of planning decisions, state-of-the art updating techniques for resource/reserve models. The impact of the project is expected on the environment through a reduction in CO2-emissions, increased energy efficiency and production of zero waste by maximizing process efficiency and resource utilization. Currently economically marginal deposits or difficult to access deposits will be become industrial viable. This will result in a sustainable increase in the competitiveness of the European raw material extraction through a reduced dependency on raw materials from non-EU sources.