Abstract
Three-dimensional (3D) surface imaging systems have gained popularity in monitoring the status and condition of separation processes by characterizing the internal and external structures of particles such as size, shape, density and composition. This review article mainly focuses on demonstrating the state of the art of 3D imaging systems in mineral processing based of the recent growth in 3D sensors. The structure of this manuscript comprises an overview of the two 3D imaging systems, including RhoVol and X-ray computed tomography, based on the basic principles. Their applications in mineral processing are then explained. By integrating with other imaging tools and the 3D printing technology, 3D surface imaging systems will play an important role in the automation and control of mineral processing in the future.
Funder
Ministry of Education, Science and Technological Development of the Republic of Serbia
Publisher
Centre for Evaluation in Education and Science (CEON/CEES)
Reference73 articles.
1. Nakhaei, F., Rahimi, S., Fathi, M. (2022B). Prediction of Sulfur Removal from Iron Concentrate Using Column Flotation Froth Features: Comparison of k-Means Clustering, Regression, Backpropagation Neural Network, and Convolutional Neural Network. Minerals, 12, 1434;
2. Nakhaei, F., Irannajad, M. (2015). Application and comparison of RNN, RBFNN and MNLR approaches on prediction of flotation column performance. Int. J. Min. Sci. Technol., 25, 983-990;
3. Nakhaei, F., Irannajad, M., Mohammadnejad, S. (2021). Application of image analysis systems in flotation process. Soft Comput. J. 2021, 5, 66-83;
4. Jovanović, I., Nakhaei, F., Kržanović, D., Conić, V., Urošević, D. (2022). Comparison of Fuzzy and Neural Network Computing Techniques for Performance Prediction of an Industrial Copper Flotation Circuit. Minerals, 12, 1493;
5. Morar, S.H., Harris, M.C., Bradshaw, D.J. (2012). The use of machine vision to predict flotation performance. Miner. Eng., 36-38, 31-36;