The Potential of Machine Learning for a More Responsible Sourcing of Critical Raw Materials

Global manufacturing firms are engaging distant suppliers of critical raw materials to participate in responsible sourcing. Downstream firms are concerned about risks in mineral supply chains of violent conflict, human rights violations, and poor governance, but they are limited in seeing their suppliers. Descriptive data on 323 smelters and refiners of tantalum, tin, tungsten, and gold (the “conflict minerals”) were complemented by interviews with downstream firms in the electronics industry. Results provided a narrative of supplier engagement, describing tactics used to identify “deep suppliers” at chokepoints in metals supply and to persuade producers into joining due diligence programs. Top-tier firms collaborate through a standards program to overcame barriers of geography and cultural distance in supply chain management beyond the visible horizon. Curiously, manufacturers do not need line-of-sight transparency to lower-tier suppliers. Rather, top-tier firms are “jumping the chain” to engage directly with “deep suppliers” who may—or may not—be their own actual physical suppliers. The research contributes empirical evidence to understanding multi-tier supply chains, examines how power is exercised by top-tier firms managing suppliers, and provides insights on supply chain transparency. Responsible sourcing, based on due diligence guidance and standards, is becoming expected of companies that are involved in supply chains of raw materials.

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Potential Benefits and Constraints of Development of Critical Raw Materials’ Production in the EU: Analysis of Selected Case Studies

Resources ◽

10.3390/resources10070067 ◽

2021 ◽

Vol 10 (7) ◽

pp. 67

Author(s):

Katarzyna Guzik ◽

Krzysztof Galos ◽

Alicja Kot-Niewiadomska ◽

Toni Eerola ◽

Pasi Eilu ◽

...

Keyword(s):

Case Studies ◽

Social Acceptance ◽

Raw Materials ◽

Local Level ◽

Ore Deposits ◽

Abandoned Mines ◽

Social Constraints ◽

Cost And Benefit Analysis ◽

The Eu ◽

Critical Raw Materials

Major benefits and constraints related to mineral extraction within the EU have been identified on the examples of selected critical raw materials’ deposits. Analyzed case studies include the following ore deposits: Myszków Mo-W-Cu (Poland), Juomasuo Au-Co (Finland), S. Pedro das Águias W-Sn (Portugal), Penouta Nb-Ta-Sn (Spain), Norra Kärr REEs (Sweden) and Trælen graphite (Norway). They represent different stages of development, from the early/grassroot exploration stage, through advanced exploration and active mining, up to reopening of abandoned mines, and refer to different problems and constraints related to the possibility of exploitation commencement. The multi-criteria analysis of the cases has included geological and economic factors as well as environmental, land use, social acceptance and infrastructure factors. These factors, in terms of cost and benefit analysis, have been considered at three levels: local, country and EU levels. The analyzed cases indicated the major obstacles that occur in different stages of deposit development and need to be overcome in order to enable a new deposit exploitation commencement. These are environmental (Juomasuo and Myszków), spatial (Juomasuo) as well as social constraints (Norra Kärr, Juomasuo). In the analyzed cases, the most important constraints related to future deposit extraction occur primarily at a local level, while some important benefits are identified mainly at the country and the EU levels. These major benefits are related to securing long-term supplies for the national industries and strategically important EU industry sectors.

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The Critical Raw Materials Issue between Scarcity, Supply Risk, and Unique Properties

Materials ◽

10.3390/ma14081826 ◽

2021 ◽

Vol 14 (8) ◽

pp. 1826

Author(s):

Mihaela Girtan ◽

Antje Wittenberg ◽

Maria Luisa Grilli ◽

Daniel P. S. de Oliveira ◽

Chiara Giosuè ◽

...

Keyword(s):

Scientific Community ◽

Chemical Elements ◽

Raw Materials ◽

The Other ◽

Innovation Networks ◽

Supply Risk ◽

European Cooperation ◽

Research And Innovation ◽

The Earth ◽

Critical Raw Materials

This editorial reports on a thorough analysis of the abundance and scarcity distribution of chemical elements and the minerals they form in the Earth, Sun, and Universe in connection with their number of neutrons and binding energy per nucleon. On one hand, understanding the elements’ formation and their specific properties related to their electronic and nucleonic structure may lead to understanding whether future solutions to replace certain elements or materials for specific technical applications are realistic. On the other hand, finding solutions to the critical availability of some of these elements is an urgent need. Even the analysis of the availability of scarce minerals from European Union sources leads to the suggestion that a wide-ranging approach is essential. These two fundamental assumptions represent also the logical approach that led the European Commission to ask for a multi-disciplinary effort from the scientific community to tackle the challenge of Critical Raw Materials. This editorial is also the story of one of the first fulcrum around which a wide network of material scientists gathered thanks to the support of the funding organization for research and innovation networks, COST (European Cooperation in Science and Technology).

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A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry

Minerals ◽

10.3390/min11080816 ◽

2021 ◽

Vol 11 (8) ◽

pp. 816

Author(s):

Mohammad Jooshaki ◽

Alona Nad ◽

Simon Michaux

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Raw Materials ◽

Relevant Literature ◽

Geological Structure ◽

Subject Area ◽

Ore Deposits ◽

Machine Learning Algorithms ◽

Global Demand ◽

Ore Grade

Machine learning is a subcategory of artificial intelligence, which aims to make computers capable of solving complex problems without being explicitly programmed. Availability of large datasets, development of effective algorithms, and access to the powerful computers have resulted in the unprecedented success of machine learning in recent years. This powerful tool has been employed in a plethora of science and engineering domains including mining and minerals industry. Considering the ever-increasing global demand for raw materials, complexities of the geological structure of ore deposits, and decreasing ore grade, high-quality and extensive mineralogical information is required. Comprehensive analyses of such invaluable information call for advanced and powerful techniques including machine learning. This paper presents a systematic review of the efforts that have been dedicated to the development of machine learning-based solutions for better utilizing mineralogical data in mining and mineral studies. To that end, we investigate the main reasons behind the superiority of machine learning in the relevant literature, machine learning algorithms that have been deployed, input data, concerned outputs, as well as the general trends in the subject area.

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Complex Concentrated Alloys for Substitution of Critical Raw Materials in Applications for Extreme Conditions

Materials ◽

10.3390/ma14051197 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1197

Author(s):

Dumitru Mitrica ◽

Ioana Cristina Badea ◽

Beatrice Adriana Serban ◽

Mihai Tudor Olaru ◽

Denisa Vonica ◽

...

Keyword(s):

Raw Materials ◽

Extreme Environment ◽

Extreme Conditions ◽

The European Union ◽

Critical Metals ◽

Research Directions ◽

Level Of Involvement ◽

General Evaluation ◽

Critical Metal ◽

Critical Raw Materials

The paper is proposing a mini-review on the capability of the new complex concentrated alloys (CCAs) to substitute or reduce the use of critical raw materials in applications for extreme conditions. Aspects regarding the regulations and expectations formulated by the European Union in the most recent reports on the critical raw materials were presented concisely. A general evaluation was performed on the CCAs concept and the research directions. The advantages of using critical metals for particular applications were presented to acknowledge the difficulty in the substitution of such elements with other materials. In order to establish the level of involvement of CCAs in the reduction of critical metal in extreme environment applications, a presentation was made of the previous achievements in the field and the potential for the reduction of critical metal content through the use of multi-component compositions.

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Dynamic estimation of future obsolete laptop flows and embedded critical raw materials: The case study of Greece

Waste Management ◽

10.1016/j.wasman.2021.07.017 ◽

2021 ◽

Vol 132 ◽

pp. 74-85

Author(s):

Eleni Kastanaki ◽

Apostolos Giannis

Keyword(s):

Raw Materials ◽

Dynamic Estimation ◽

Of Greece ◽

Critical Raw Materials

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Strategies for fulfilment of critical raw materials demand in Europe

Revue de Métallurgie ◽

10.1051/metal/2012035 ◽

2012 ◽

Vol 109 (5) ◽

pp. 333-339 ◽

Cited By ~ 4

Author(s):

D. Senk ◽

F.M. Meyer ◽

T. Pretz ◽

G. Abrasheva

Keyword(s):

Raw Materials ◽

Critical Raw Materials

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A proof of concept for machine learning-based virtual knapping using neural networks

Scientific Reports ◽

10.1038/s41598-021-98755-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jordy Didier Orellana Figueroa ◽

Jonathan Scott Reeves ◽

Shannon P. McPherron ◽

Claudio Tennie

Keyword(s):

Machine Learning ◽

Raw Materials ◽

Training Sample ◽

Stone Tool ◽

Proof Of Concept ◽

Surface Information ◽

Archaeological Data ◽

Testing Dataset ◽

Intact Core ◽

Key Variables

AbstractPrehistoric stone tools are an important source of evidence for the study of human behavioural and cognitive evolution. Archaeologists use insights from the experimental replication of lithics to understand phenomena such as the behaviours and cognitive capacities required to manufacture them. However, such experiments can require large amounts of time and raw materials, and achieving sufficient control of key variables can be difficult. A computer program able to accurately simulate stone tool production would make lithic experimentation faster, more accessible, reproducible, less biased, and may lead to reliable insights into the factors that structure the archaeological record. We present here a proof of concept for a machine learning-based virtual knapping framework capable of quickly and accurately predicting flake removals from 3D cores using a conditional adversarial neural network (CGAN). We programmatically generated a testing dataset of standardised 3D cores with flakes knapped from them. After training, the CGAN accurately predicted the length, volume, width, and shape of these flake removals using the intact core surface information alone. This demonstrates the feasibility of machine learning for investigating lithic production virtually. With a larger training sample and validation against archaeological data, virtual knapping could enable fast, cheap, and highly-reproducible virtual lithic experimentation.

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Machine Learning Methods Applied for Modeling the Process of Obtaining Bricks Using Silicon-Based Materials

Materials ◽

10.3390/ma14237232 ◽

2021 ◽

Vol 14 (23) ◽

pp. 7232

Author(s):

Costel Anton ◽

Silvia Curteanu ◽

Cătălin Lisa ◽

Florin Leon

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Raw Materials ◽

Optimization Procedure ◽

Exhaust Emission ◽

Energy Potential ◽

Learning Methods ◽

Machine Learning Methods ◽

Emission Changes ◽

The Impact

Most of the time, industrial brick manufacture facilities are designed and commissioned for a particular type of manufacture mix and a particular type of burning process. Productivity and product quality maintenance and improvement is a challenge for process engineers. Our paper aims at using machine learning methods to evaluate the impact of adding new auxiliary materials on the amount of exhaust emissions. Experimental determinations made in similar conditions enabled us to build a database containing information about 121 brick batches. Various models (artificial neural networks and regression algorithms) were designed to make predictions about exhaust emission changes when auxiliary materials are introduced into the manufacture mix. The best models were feed-forward neural networks with two hidden layers, having MSE < 0.01 and r2 > 0.82 and, as regression model, kNN with error < 0.6. Also, an optimization procedure, including the best models, was developed in order to determine the optimal values for the parameters that assure the minimum quantities for the gas emission. The Pareto front obtained in the multi-objective optimization conducted with grid search method allows the user the chose the most convenient values for the dry product mass, clay, ash and organic raw materials which minimize gas emissions with energy potential.

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WEEE collection and CRM recovery trials: piloting a holistic approach for Scotland

Global NEST Journal ◽

10.30955/gnj.002643 ◽

2018 ◽

Vol 20 (4) ◽

pp. 712-718

Keyword(s):

Raw Materials ◽

Scale Up ◽

Holistic Approach ◽

Extraction Methods ◽

Social Enterprises ◽

Raw Material ◽

Household Waste ◽

Sustainable Supply Chain ◽

Gold Silver ◽

Critical Raw Materials

<p>Re-Tek UK and its partners, Enscape Consulting and the University of West of Scotland commenced trials for the collection and recovery of critical raw materials from waste electrical and electronic (WEEE) products in July 2016. Sponsored by the EU LIFE funded project ‘Critical Raw Material Closed Loop Recovery’ coordinated by WRAP with EARN, ERP UK Ltd, KTN Ltd and Wuppertal Institute as beneficiaries. The trials are aimed at boosting the recovery of critical raw materials (CRMs) from household waste electrical and electronic products (WEEE) and Information Communications Technology (ICT) in particular, after functioning equipment is separated out for re-use. The new collection models provided residents with the opportunity to drop-off unwanted electrical and electronic appliances at a time and place that suits them, through a collaborative approach which encourages local authorities, educational establishments, businesses, and Social Enterprises, etc to act as hub sites. Hubs were designed to minimize product damage and encourage drop-off, rather than hoarding. Extraction methods developed after the collection phase of the trial looked at the opportunity to recover cobalt, gold and silver from ICT products, with the potential to inform how a more sustainable supply chain could be developed in Scotland. The elements studied were selected to demonstrate financial opportunity (gold/silver) and a strategic priority material (cobalt) for long term supply. These are based on bioleaching and electrochemical recovery using novel carbon based electrode systems, and chemical processing methods using extraction techniques with an assessment of pilot performance and scale up challenges. Our report is on the state of progress towards practical solutions to WEEE and CRM recovery.</p>

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