Reprint

Envisioning the Future of Mining

Edited by
January 2024
200 pages
  • ISBN978-3-0365-9941-0 (Hardback)
  • ISBN978-3-0365-9942-7 (PDF)

This book is a reprint of the Special Issue Envisioning the Future of Mining that was published in

Engineering
Environmental & Earth Sciences
Summary

To date, many challenges have been faced in the production of mineral raw materials to provide necessary supplies in almost any production chain. In addition to the traditional search for more efficient processes, cleaner and safer operations, and higher levels of social acceptance, there is a need for the mining industry to become a major actor in circular economy, decarbonization, and digital transformation processes. This reprint includes the articles published in the Special Issue of Mining entitled "Envisioning the Future of Mining".

Format
  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
Keywords
surge loader; mine safety; load and haul; truck and shovel; open pit mining; haulage systems; mining hazard; safe mining; miners; artisanal barite mining; mining sites; dumping; digital transformation; high-fidelity modelling; rock fragmentation prediction; machine learning; Kuz–Ram model; fragmentation models; fragment size distribution; artificial neural network; support vector regression; blasting; open-pit mines; multi-criteria decision analysis; AHP; Python; minor critical metals; mining co- and by-products; machine learning; XGBoost algorithm; PERCLOS system; fatigue; decarbonization; IPCC; trolley assist; battery trolley; battery-electric trucks; electrification alternatives; urban mining; circular economy; sustainable development; engineering education; humanitarian engineering; community development; engineering education; sociotechnical thinking; global sociotechnical competency; corporate social responsibility; sustainability in mining engineering education; open-pit phosphate mine; phosphate ore screening unit; anomaly detection; intelligent monitoring system; machine learning; deep learning; n/a

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