Deep convolutional neural network for mill feed size characterization
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Date
Authors
Olivier, Laurentz Eugene
Maritz, M.G. (Michael)
Craig, Ian Keith
Journal Title
Journal ISSN
Volume Title
Publisher
International Federation of Automatic Control
Abstract
Knowing the characteristics of the feed ore size is an important consideration for operations and control of a run-of-mine ore milling circuit. Large feed ore variations are important to detect as they require intervention, whether it be manual by the operator or by an automatic controller. A deep convolutional neural network is used in this work to classify the feed ore images into one of four classes. A VGG16 architecture is used and the classifier is trained making use of transfer learning.
Description
Keywords
Deep learning, Convolutional neural network, Transfer learning, Milling, Run-of-mine ore
Sustainable Development Goals
Citation
Olivier, L.E., Maritz, M.G. & Craig, I.K. 2019, 'Deep convolutional neural network for mill feed size characterization', IFAC-PapersOnLine, vol. 54, no. 14, pp. 105-110.