Reprint
Advances in Computational Intelligence Applications in the Mining Industry
Edited by
February 2022
324 pages
- ISBN978-3-0365-3159-5 (Hardback)
- ISBN978-3-0365-3158-8 (PDF)
This is a Reprint of the Special Issue Advances in Computational Intelligence Applications in the Mining Industry that was published in
Chemistry & Materials Science
Engineering
Environmental & Earth Sciences
Summary
This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners.
Format
- Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
Keywords
truck dispatching; mining equipment uncertainties; orebody uncertainty; discrete event simulation; Q-learning; grinding circuits; minerals processing; random forest; decision trees; machine learning; knowledge discovery; variable importance; mineral prospectivity mapping; random forest algorithm; machine learning; epithermal gold; unstructured data; machine learning; blast impact; empirical model; mining; fragmentation; machine learning; mine worker fatigue; random forest model; health and safety management; stockpiles; operational data; mine-to-mill; geostatistics; ore control; mine optimization; discrete event simulation; digital twin; modes of operation; geological uncertainty; multivariate statistics; partial least squares regression; oil sands; bitumen extraction; bitumen processability; mine safety and health; accidents; narratives; machine learning; natural language processing; random forest classification; hyperspectral imaging; multispectral imaging; dimensionality reduction; neighbourhood component analysis; artificial intelligence; machine learning; mining exploitation; masonry buildings; damage risk analysis; artificial intelligence; Bayesian network; Naive Bayes; Bayesian Network Structure Learning (BNSL); machine learning; random forest; rock type; mining geology; bluetooth beacon; classification and regression tree; gaussian naïve bayes; k-nearest neighbors; support vector machine; transport route; transport time; underground mine; tactical geometallurgy; data analytics in mining; ball mill throughput; measurement while drilling; non-additivity; coal; petrographic analysis; macerals; image analysis; semantic segmentation; convolutional neural networks; point cloud scaling; fragmentation size analysis; structure from motion; n/a