Mining Goes Digital

Mueller, Christoph; Assibey-Bonsu, W.; Baafi, Ernest; Dauber, Christoph; Doran, C; Jaszczuk, Marek Jerzy; Nagovitsyn, O. V.

doi:10.1201/9780429320774

Cited by 5 publications

(3 citation statements)

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“…Data abundance, low computing costs, and advances in digital technologies (machine learning, artificial intelligence, Internet of Things [IoT], and big data), as well as biotechnology, and nanotechnology combined with environmental and social pressures have opened opportunities for learning and innovation through challenges in mining production Katz and Pietrobelli 2018). Examples include advanced business decisions, autonomous, self-controlled devices and processes, flexible business operations adaptation, human resource planning, and machine-learning algorithms to predict tasks (Gružauskasa et al 2018),4 helping to boost productivity and mitigate social and environmental impacts (Humphreys 2018;Mueller et al 2019;Tribal 2018), and industry reorganization to support knowledge-intensive mining services (KIMS) (Scott-Kemmis 2013), as in Australia (Scott-Kemmis 2013), South Africa (Kaplan 2012), and Sweden (Nuur et al 2018).…”

Section: Natural Resource-intensive Industries and Mining: A Brief Ov...mentioning

confidence: 99%

“…The 39th APCOM (Applications for Computers and Operations Research in the Minerals Industry) conference entitled "Mining Goes Digital" presented innovative IT-related papers from resource estimation and geostatistics, mine planning, robotics, equipment automation, autonomous guidance, and many other integrative aspects of digital transformation in the minerals industry (seeMueller et al 2019). …”

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confidence: 99%

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The Challenges of Technology and Economic Catch-up in Emerging Economies

2021

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This book synthesizes and interprets existing knowledge on technology upgrading failures as well as lessons from successes and failures in order to better understand the challenges of technology upgrading in emerging economies. The objective is to bring together in one volume diverse evidence regarding three major dimensions of technology upgrading: paths of technology upgrading, structural changes in the nature of technology upgrading, and the issues of technology transfer and technology upgrading. The knowledge of these three dimensions is being synthesized at the firm, sector, and macro levels across different countries and world macro-regions. Compared to the old and new challenges and uncertainties facing emerging economies, our understanding of the technology upgrading is sparse, unsystematic, and scattered. While our understanding of these issues from the 1980s and 1990s is relatively more systematized, the changes that took place during the globalization and proliferation of GVCs, the effects of the post-2008 events, and the effects of the current COVID-19 and geopolitical struggles on technology upgrading have not been explored and compared synthetically. Moreover, the recent growth slowdown in many emerging economies, often known as a middle-income trap, has reinforced the importance of understanding the technology upgrading challenges of catching-up economies. We believe that the time is ripe for “taking stock of the area” in order to systematize and evaluate the existing knowledge on processes of technology upgrading of emerging economies at the firm, sector, and international levels and to make further inroads in research on this issue. This volume aims to significantly contribute towards this end.

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Section: Natural Resource-intensive Industries and Mining: A Brief Ov...mentioning

confidence: 99%

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confidence: 99%

The Challenges of Technology and Economic Catch-up in Emerging Economies

2021

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“…Constructing the right flow anamorphosis transformation is of special importance in mineral resource estimation since different data sets are treated during the run of the mine (Mariz et al 2019). These are obtained by different sampling methods and sensor devices.…”

Section: Flow Anamorphosismentioning

confidence: 99%

Resource Model Updating For Compositional Geometallurgical Variables

et al. 2020

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In the field of mineral resources extraction, one main challenge is to meet production targets in terms of geometallurgical properties. These properties influence the processing of the ore and are often represented in resource modeling by coregionalized variables with a complex relationship between them. Valuable data are available about geometalurgical properties and their interaction with the beneficiation process given sensor technologies during production monitoring. The aim of this research is to update resource models as new observations become available. A popular method for updating is the ensemble Kalman filter. This method relies on Gaussian assumptions and uses a set of realizations of the simulated models to derive sample covariances that can propagate the uncertainty between real observations and simulated ones. Hence, the relationship among variables has a compositional nature, such that updating these models while keeping the compositional constraints is a practical requirement in order to improve the accuracy of the updated models. This paper presents an updating framework for compositional data based on ensemble Kalman filter which allows us to work with compositions that are transformed into a multivariate Gaussian space by log-ratio transformation and flow anamorphosis. This flow anamorphosis, transforms the distribution of the variables to joint normality while reasonably keeping the dependencies between components. Furthermore, the positiveness of those variables, after updating the simulated models, is satisfied. The method is implemented in a bauxite deposit, demonstrating the performance of the proposed approach.

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Development of Big Data Analytics in a Multi-site Enterprise on the Example of Supply Chain Management

Pyda

Stefaniak

Dudycz

et al. 2021

IFIP Advances in Information and Communication Technology

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Currently, advanced data analytics in large multi-site industrial enterprises is a strategic element in making management decisions. Integrated supply chain management (SCM), machinery park management, or data analysis from industrial devices (including using Industrial Internet of Things -IIoT) requires the organization of appropriate analytical platform architecture, the selection of the analytical tools for Big Data, the implementation of advanced algorithms based on machine learning and the development of management dashboards for ongoing tracking the KPI's of assets. This article presents the issues related to the acquisition, analysis, and management of large amounts of data from various enterprise departments. These data come from multiple systems, and they are indifferent data recording standards. They are essential because they form the basis of advanced data analysis in supply chain management in multi-site enterprises. This article discusses the proposal of an analytical platform for SCM and the development of analytical processing for SCM in the multi-site industrial enterprise.

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Mining Goes Digital

Cited by 5 publications

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The Challenges of Technology and Economic Catch-up in Emerging Economies

The Challenges of Technology and Economic Catch-up in Emerging Economies

Resource Model Updating For Compositional Geometallurgical Variables

Development of Big Data Analytics in a Multi-site Enterprise on the Example of Supply Chain Management

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