MetalIntelligence SIGNED
European Industrial Doctorate in future efficient minerals analysis, processing and training
Total Cost €
0EC-Contrib. €
0Partnership
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0MetalIntelligence project word cloud
Explore the words cloud of the MetalIntelligence project. It provides you a very rough idea of what is the project "MetalIntelligence" about.
Project "MetalIntelligence" data sheet
The following table provides information about the project.
| Coordinator |
THE PROVOST, FELLOWS, FOUNDATION SCHOLARS & THE OTHER MEMBERS OF BOARD OF THE COLLEGE OF THE HOLY & UNDIVIDED TRINITY OF QUEEN ELIZABETH NEAR DUBLIN
Organization address contact info |
| Coordinator Country | Ireland [IE] |
| Project website | http://www.metalintelligence.eu |
| Total cost | 1˙596˙958 € |
| EC max contribution | 1˙596˙958 € (100%) |
| Programme |
1. H2020-EU.1.3.1. (Fostering new skills by means of excellent initial training of researchers) |
| Code Call | H2020-MSCA-ITN-2016 |
| Funding Scheme | MSCA-ITN-EID |
| Starting year | 2016 |
| Duration (year-month-day) | from 2016-09-01 to 2020-08-31 |
Partnership
Take a look of project's partnership.
| # | ||||
|---|---|---|---|---|
| 1 | THE PROVOST, FELLOWS, FOUNDATION SCHOLARS & THE OTHER MEMBERS OF BOARD OF THE COLLEGE OF THE HOLY & UNDIVIDED TRINITY OF QUEEN ELIZABETH NEAR DUBLIN | IE (DUBLIN) | coordinator | 531˙349.00 |
| 2 | OUTOTEC (FINLAND) OY | FI (ESPOO) | participant | 538˙290.00 |
| 3 | LULEA TEKNISKA UNIVERSITET | SE (LULEA) | participant | 527˙318.00 |
| 4 | OUTOTEC OYJ | FI (ESPOO) | participant | 0.00 |
| 5 | AGNICO-EAGLE FINLAND OY | FI (KIISTALA) | partner | 0.00 |
| 6 | IMA Engineering LtD Oy | FI (ESPOO) | partner | 0.00 |
| 7 | OXFORD INSTRUMENTS NANOTECHNOLOGY TOOLS LIMITED | UK (ABINGDON) | partner | 0.00 |
| 8 | Teck Ireland | IE (Wicklow) | partner | 0.00 |
Map
Project objective
MetalIntelligence proposes an EID in future efficient minerals analysis, processing and training. Transformational change caused by the third industrial revolution, which itself relies on extraction of raw materials, provides the impetus. A resource-efficient EU is recognised as a societal challenge. However, the pathway from searching for mineral deposits to metal production is complex and involves a number of sectors (geosciences, chemical analysts, mining, processing, metallurgy) that have tended to work in silos. Limited intersectoral collaboration has led to compartmentalised thinking which has become an impediment to improved resource and energy efficiency. The overall aim of MetalIntelligence is to disrupt this compartmentalisation, by training 6 ESRs through a multi-disciplinary network of academic and industry leaders with an exceptionally broad range of expertise and skills. The team comprises three beneficiaries (2 academic, in Ireland and Sweden, and one Industry, Finland) as well as 4 partners (from the UK, Ireland, and Finland) who will provide secondment opportunities and/or industrial supervision. The programme is arranged as work packages (WPs), including management, training, and dissemination as well as three research-driven packages. The first research-based WP aims to improve mineral analysis in preparation for more efficient processing. The second concerns itself with advanced modelling of dynamic minerals processing. The third will research modern, technology-enhanced training for future professionals. The tailor-made training programme consists of network wide components, delivered by industry and academia leaders as well as specific training in the workplace. The objectives of MetalIntelligence were designed to align with European innovation capacity policies and the network will contribute to keeping the industries competitive by developing innovative, more gender-balanced human capital for the raw materials sector in Europe.
Deliverables
| Induction meeting | Other | 2019-07-05 12:31:16 |
| ESR position advertised | Other | 2019-07-05 12:31:16 |
Take a look to the deliverables list in detail: detailed list of MetalIntelligence deliverables.
Publications
| year | authors and title | journal | last update |
|---|---|---|---|
| 2019 |
Pratama Istiadi Guntoro, Glacialle Tiu, Yousef Ghorbani, Cecilia Lund, Jan Rosenkranz Application of machine learning techniques in mineral phase segmentation for X-ray microcomputed tomography (µCT) data published pages: 105882, ISSN: 0892-6875, DOI: 10.1016/j.mineng.2019.105882 |
Minerals Engineering 142 | 2020-02-13 |
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The information about "METALINTELLIGENCE" are provided by the European Opendata Portal: CORDIS opendata.
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