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Digital extraction: Blockchain traceability in mineral supply chains
استخراج دیجیتال: قابلیت ردیابی بلاکچین در زنجیره های تأمین مواد معدنی-2021 Digital data — including technologically-mediated data generated by blockchain-enabled traceability — is performing an increasingly integral role in extractive operations, but scarce attention has been paid to the structuring effect of these digital technologies or the socio-economic spatiality of data-driven mining operations. Drawing on extensive qualitative research (interviews, participant observation, and two sets of survey data among actors relevant to these mineral supply chains), this article advances the notion of “digital extraction” todescribe the collection, analysis, and instrumentalization of digital data generated under the banner of blockchain-based due diligence, chain of custody certifications, and various transparency mechanisms, situated alongside and in support of mineral extraction. The article mobilizes concepts from political geography and political ecology to argue that digital technologies of traceability in extractive processes potentially create new forms of control and exclusion or exacerbate existing social, political, and territorial dispossession through asymmetric relations of power and knowledge in mineral supply chains. Despite industry efforts to make mineral supply chains more sustainable by resorting to digital certification and traceability, the strategic uses of un- certainty, ignorance, and ambiguity undergirding blockchain-enabled traceability systems fail to challenge existing inequalities in resource use and access or fulfill the promise of transparency and accountability. Keywords: Blockchain | Traceability | Mining | Digital extraction | Certification | Digital technology | Political ecology |
مقاله انگلیسی |
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Digging for due diligence: The case of non-state mineral supply chain regulation by ITSCI in Rwanda
حفاری برای دقت لازم: مورد تنظیم زنجیره تأمین مواد معدنی غیر دولتی توسط ITSCI در رواندا-2021 Today’s complex mineral supply chains make it difficult to hold private actors to account in case they breach regulations. Non-state actors increasingly make efforts to help regulate these mineral supply chains via due diligence programmes. The purpose of this study is to investigate how non-state mineral supply chain regulation functions on the ground, and whether and under what conditions non-state actors can hold private actors to account. Based on an in-depth case study of the ITSCI programme in Rwanda, we demonstrate that although non- state regulation of mineral supply chains has huge potential, the ITSCI programme faces several challenges. We find that there are four conditions to be met for non-state actors to hold private supply chain actors to account: 1) the programme should provide clear and timely information to all stakeholders; 2) high-quality and frequent monitoring should be ensured; 3) there should be a possibility of imposing credible sanctions; and 4) the governance of the programme should act in the public interest. On the basis of our research it is reasonable to conclude that the ITSCI programme meets the third condition on sanctions, but that it faces a number of challenges with respect to the first, second and fourth condition. Keywords: Accountability | Mineral supply chains | Due diligence | ITSCI | Rwanda |
مقاله انگلیسی |
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Conflict minerals and battery materials supply chains: A mapping review of responsible sourcing initiatives
زنجیره های تأمین مواد معدنی و مواد باتری درگیری: بررسی نقشه ابتکارهای مسئول تهیه منابع-2021 Responsible mineral sourcing is a major issue on the global sustainability agenda. Spurred by “conflict minerals”, debates about the ethics of mineral supply chains now encompass a broad set of concerns including child labor, corruption, environmental degradation, and a green transition away from fossil fuels. The past two decades have seen a flurry of initiatives to clean up supply chains and protect the reputation of major companies. Based on a mapping review of 220 studies of responsible mineral supply chains, this study highlights the approaches that responsible minerals sourcing initiatives have taken, focusing on conflict minerals (tin, tungsten tantalum and gold) as well as metals and minerals needed for renewable energy technologies in a transition to a low carbon economy (cobalt, graphite, lithium, manganese and nickel). We briefly describe the evolution of these initiatives, contrast arguments about mandatory and voluntary approaches, summarize findings regarding their impacts on local communities and corporate actors, and discuss the challenges and opportunities of new technologies and traceability systems. Keywords: Responsible sourcing | CSR | Supply chains | Blockchain | Conflict minerals | Cobalt |
مقاله انگلیسی |
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Narrative accounting for mining in Ghana: An old defence against a new threat?
حسابداری روایت برای معدن در غنا: دفاع قدیمی در برابر یک تهدید جدید؟-2021 This article is concerned with aspects of how accounting and accountants figure in economics and policy issues
related to mineral and fossil fuel extraction, production and use. Starting by appraising whether narrative ac-
counting by a transnational mining corporation is attuned to the people working or living in an area affected by
the mining operations, it goes on to considering how data, calculations and communications pertaining to sus-
tainability are applied. This includes what connections the people involved perceive between accounting and
sustainability. Data were obtained through qualitative fieldwork in and around the Damang Mine in Ghana,
comprising interviews with employees and in the community, and analysis of documents. Corporation executives
use narrative accounting to back claims that they invest hugely in sustainability, so having, in their words, a
social licence to operate from host community stakeholders. This reflects accounting figuring in resource allocation
choices, including in terms of how shareholder capital is managed to generate greater societal value and to
operate sustainably. However, although many local people see themselves as deriving some benefit from the
socio-economic activities of the mining corporation, they see accounting as not their business, being more
economic than environmental or social. The inference is that accounting continues to serve purposes of man-
agement control of production, distributing value-added in favour of capital providers and managing image
reflected in the notion of having a social licence to operate. Despite their belief that accounting and accountants
having roles to play in sustainability, they generally cannot identify these roles. These findings imply that, if
account providers are serious about being corporately responsible towards affected people, they must do more to
ensure that environmental and social aspects receive enough attention to convince those people that they are
truly being engaged with on equal terms, in addition to convincing a wider audience that the reports they
produce are reliable and relevant to sustainability in practice. keywords: پایداری | حسابداری | غانیان | بخش معدن | مجوز اجتماعی | Sustainability | Accounting | Ghanaians | Mining sector | Social licence |
مقاله انگلیسی |
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Two dimensional joint inversion of direct current resistivity and radiomagnetotelluric data based on unstructured mesh
وارونگی مشترک دو بعدی مقاومت جریان مستقیم و داده های رادیوماگنتوتلوژنی بر اساس مش بدون ساختار-2020 Using the unstructured mesh, a new two-dimensional joint inversion algorithm has been developed for
Radiomagnetotelluric and Direct current resistivity data. The unstructured mesh is generated with triangular
cells, whose vertical and lateral lengths increase towards the depths. The Finite Element Method (FEM) has
been used in the forward modelling part of the developed joint inversion algorithm. In the previous studies,
structured grid-based joint inversion algorithms have been developed using the Finite Difference Method
(FDM). In the structured grid-based algorithms, when the mesh is being generated with rectangular cells, the
vertical lengths of the cells get bigger towards the depths while the lateral lengths remain constant. With the
structured mesh, the undulated surface topography cannot be represented well enough. Also, because of the incompatible
aspect ratio ofmodel cell sizes in deepermodel sections, the resolution of themodel parameters will
get smaller and cannot be resolved well with the structured grids. Imaging of surface topography and underground
resistivity structures by the new algorithm requires fewer elements than those using structured grids.
Therefore, the developed algorithm is faster than traditional 2D inversion algorithms. Furthermore, the resolution
of the deeper model parameters has been increased by using the definition of the unstructured grid. A regularized
inversion scheme with a smoothness-constrained stabilizer has been employed to invert the data. First,
we have tested the developed joint inversion algorithm using synthetic data simplified from archaeological and
mine site scenario and the results have been compared with the conventional algorithms using structured grids.
We have also tested our algorithmwith the real data which were collected frommineral investigation site at approximately
10 kmeast of the Elbistan district of Kahramanmaraş province, in thewest of the TaurusMountains,
Turkey. The results show that the developed joint inversion algorithm is a powerful tool to detect both resistive
and conductive targets. Keywords: Direct current resistivity | Radiomagnetotelluric | Joint Inversion | Mineral Exploration | Unstructured mesh | Modelling |
مقاله انگلیسی |
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Using machine learning to estimate a key missing geochemical variable in mining exploration: Application of the Random Forest algorithm to multisensor core logging data
استفاده از یادگیری ماشینی برای برآورد متغیر ژئوشیمیایی از دست رفته کلیدی دراستخراج اکتشاف : کاربرد الگوریتم جنگل تصادفی به داده های ورود به سیستم چند هسته ای-2019 Mining exploration increasingly relies on large, multivariate databases storing data ranging from drill core
geochemical analysis to geophysical data or geological descriptions. Utilizing these large datasets to their full
potential implies the use of multivariate statistical analysis such as machine learning. The Random Forest algorithm
has proved its efficiency in mining applications. In this study we use it to estimate a key geochemical
element, sodium, using a multivariate chemo-physical dataset measured on drill cores in the Matagami mining
district of Québec, Canada. Sodium is important to characterize hydrothermal alteration in volcanogenic massive
sulfide settings, since Na depletion can be used to vector towards ore, but this element is not readily measured by
portable X-ray fluorescence (pXRF). We first test the algorithm on a database of over 8000 traditional laboratory
geochemistry analyses and find a correlation of 0.95 between estimated and measured Na. We then test the
algorithm on the multi-sensor core logging data, including density, magnetic susceptibility, and 15 geochemical
elements by pXRF, but borrowing Na from traditional geochemistry (n=260). This yields correlations of 0.66 to
0.75 depending on the training and testing sets. Finally the algorithm is applied to the whole multiparameter
database (n=9675) to estimate Na downcore. There is a good general correspondence with the downcore Na
patterns seen through traditional geochemistry, and the estimated Na which has much greater spatial resolution.
Random Forest appears to be a very good estimation tool when using large amounts of data and variables, as it
uses all variables and automatically prioritizes the most useful. This method also allows visualization of the
weight of each variable in the estimation. Future studies should compare RF with other methods. Keywords: Artificial intelligence | Geochemistry | Supervised method | Mineral exploration |
مقاله انگلیسی |
7 |
Machine learning application to automatically classify heavy minerals in river sand by using SEM/EDS data
کاربرد یادگیری ماشین برای طبقه بندی خودکار مواد معدنی سنگین در ماسه رودخانه با استفاده از داده های SEM / EDS-2019 Heavy minerals are generally trace components of sand or sandstone. Fast and accurate heavy mineral classification
has become a necessity. Energy Dispersive X-ray Spectrometers (EDS) integrated with Scanning Electron
Microscopy (SEM) were used to obtain rapid heavy mineral elemental compositions. However, mineral identification
is challenging since there are wide ranges of spectral datasets for natural minerals. This study aimed to
find a reliable, machine learning classifier for identifying various heavy minerals based on EDS data. After
selecting 22 distinct heavy minerals from modern river sands, we obtained their elemental data by SEM/EDS.
The elemental data from a total of 3067 mineral grains were collected under various instrumental conditions. We
compared the classification performance of four classifiers (Decision Tree, Random Forest, Support Vector
Machine, Bayesian Network). Our results indicated that machine learning methods, especially Random Forest,
can be used as the most effective classifier for heavy mineral classification. Keywords: Heavy mineral | Machine learning | Energy dispersive X-ray spectrometers | Sand | Classification | Sedimentology | Geology |
مقاله انگلیسی |
8 |
Machine learning models for the lattice thermal conductivity prediction of inorganic materials
مدل های یادگیری ماشین برای پیش بینی هدایت حرارتی شبکه از مواد معدنی-2019 The lattice thermal conductivity (κL) is a critical property of thermoelectrics, thermal barrier coating materials
and semiconductors. While accurate empirical measurements of κL are extremely challenging, it is usually approximated
through computational approaches, such as semi-empirical models, Green-Kubo formalism coupled
with molecular dynamics simulations, and first-principles based methods. However, these theoretical methods
are not only limited in terms of their accuracy, but sometimes become computationally intractable owing to their
cost. Thus, in this work, we build a machine learning (ML)-based model to accurately and instantly predict κL of
inorganic materials, using a benchmark data set of experimentally measured κL of about 100 inorganic materials.
We use advanced and universal feature engineering techniques along with the Gaussian process regression algorithm,
and compare the performance of our ML model with past theoretical works. The trained ML model is
not only helpful for rational design and screening of novel materials, but we also identify key features governing
the thermal transport behavior in non-metals. Keywords: Lattice thermal conductivity | Inorganic materials | Machine learning Models |
مقاله انگلیسی |
9 |
Combination of complementary data mining methods for geographical characterization of extra virgin olive oils based on mineral composition
ترکیبی از روش های داده کاوی مکمل برای تعیین ویژگی های جغرافیایی روغن زیتون فوق العاده با استفاده از ترکیبات معدنی-2018 This work explores the potential of multi-element fingerprinting in combination with advanced data mining
strategies to assess the geographical origin of extra virgin olive oil samples. For this purpose, the concentrations
of 55 elements were determined in 125 oil samples from multiple Spanish geographic areas. Several un
supervised and supervised multivariate statistical techniques were used to build classification models and in
vestigate the relationship between mineral composition of olive oils and their provenance. Results showed that
Spanish extra virgin olive oils exhibit characteristic element profiles, which can be differentiated on the basis of
their origin in accordance with three geographical areas: Atlantic coast (Huelva province), Mediterranean coast
and inland regions. Furthermore, statistical modelling yielded high sensitivity and specificity, principally when
random forest and support vector machines were employed, thus demonstrating the utility of these techniques in
food traceability and authenticity research.
Keywords: Olive oil ، Geographical traceability ، Mineral profile ، Inductively coupled plasma-mass spectrometry ، Data mining |
مقاله انگلیسی |
10 |
Production weighted water use impact characterisation factors for the global mining industry
فاكتورهاي مشخصه تأثير مصرف آب با استفاده از وزن آب براي توليد صنعت معدن جهانی-2018 Methods for quantifying the impacts of water use within life cycle assessment have developed signifi
cantly over the past decade. These methods account for local differences in hydrology and water use
contexts through the use of regionally specific impact characterisation factors. However, few studies have
applied these methods to the mining industry and so there is limited understanding regarding how
spatial boundaries may affect assessments of the mining industrys consumptive water use impacts. To
address this, we developed production weighted characterisation factors for 25 mineral and metal
commodities based upon the spatial distribution of global mine production across watersheds and na
tions. Our results indicate that impact characterisation using the national average ‘Water Stress Index’
(WSI) would overestimate the water use impacts for 67% of mining operations when compared to as
sessments using watershed WSI values. Comparatively, national average ‘Available Water Remaining’
(AWaRe) factors would overestimate impacts for 60% of mining operations compared to assessments
using watershed factors. In the absence of watershed scale inventory data, assessments may benefit from
developing alternative characterisation factors reflecting the spatial distribution of commodity produc
tion across watersheds. The results also provide an indication of the commodities being mined in highly
water stressed or scarce regions.
Keywords: Impact characterisation ، Mining industry ، Mineral and metal production ، Water stress index (WSI) ، Available water remaining ، Life cycle assessment (LCA) |
مقاله انگلیسی |