Commenced in January 2007
Paper Count: 30124
Application of Computer Aided Engineering Tools in Performance Prediction and Fault Detection of Mechanical Equipment of Mining Process Line
Abstract:Nowadays, to decrease the number of downtimes in the industries such as metal mining, petroleum and chemical industries, predictive maintenance is crucial. In order to have efficient predictive maintenance, knowing the performance of critical equipment of production line such as pumps and hydro-cyclones under variable operating parameters, selecting best indicators of this equipment health situations, best locations for instrumentation, and also measuring of these indicators are very important. In this paper, computer aided engineering (CAE) tools are implemented to study some important elements of copper process line, namely slurry pumps and cyclone to predict the performance of these components under different working conditions. These modeling and simulations can be used in predicting, for example, the damage tolerance of the main shaft of the slurry pump or wear rate and location of cyclone wall or pump case and impeller. Also, the simulations can suggest best-measuring parameters, measuring intervals, and their locations.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1125705Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 780
 E. Russell, D. Cirulis+, R. J. Maron, C. O’Keefe, P.Rothman, D. Newton, “Optimization of hydrocyclone classification by on-line detection of coarse material in the overflow stream”, Proceedings of 15th IFAC Symposium on Automation Control and Optimization in Mining, Mineral & Metal Processing, San Diego, CA. 2013.
 R. S. Beebe, Predictive maintenance of pumps using condition monitoring, Elsevier Ltd., 2004.
 Allan Thomas, “slurry systems control of tails pipeline to maximize concentration”, 3rd Annual Slurry Pipelines Summit, Australia, Perth. 2013.
 L. Svarovsky, Solid-Liquid Separation, Butterworth-Heinemann, 2000.
 Metso, Basic in mineral processing, Metso Minerals, 2002.
 ABB Limited, Hydrocyclone feed in mining industry, AG/MI101-EN, 2013.