Open Collections

Martino, Natalia

University of British Columbia

Producing mines and new projects are forced to mine lower grade and more complex ores. As a consequence, there is an increased need to monitor penalty elements. The penalty elements are the constituents that reduce the quality, and therefore the value of the concentrate. Many of these constituents are low atomic number elements that are not adequately detected with most existing sensors. The case study evaluated in this work, is an operating iron mine where silicon, aluminum, potassium, phosphorus, and sulfur are penalty elements. Real-time ore monitoring and Sensor-Based Sorting utilize different analytical techniques that serve as sensors. Many of the more studied and used analytical techniques, such as ICP (Inductively Coupled Plasma), XRD (X-Ray Diffraction), XRF (X-Ray Fluorescence), PGNAA (Prompt Gamma Neutron Activation Analysis), or XRT (X-ray Transmission), require long scanning times, time-consuming sampling procedures or cannot detect low atomic number elements. Laser Induced Breakdown Spectroscopy (LIBS) is a proven analytical technique that can analyze all elements in the periodic table in real-time. It had advanced significantly since its conception when it was a fragile laser technology. Especially after 2012 and 2021, LIBS received much attention when NASA successfully used it on the Mars rover to analyze soils and rocks at a distance. In this thesis, the experiments were done with crushed Run of Mine iron ore material to investigate the reconciliation between the bulk analysis by conventional techniques (assay) versus surface analysis provided by LIBS. Univariate and multivariate models achieved between 88 to 99% correlation with the assay results. The challenges presented during this research can be explained by low signal-to-noise ratios, self-absorption phenomena, low concentration of the element of interest, and the available low energy LIBS setup used in the study. The results presented indicate that LIBS can be used to accurately perform monitoring and sorting of crushed iron ore, with multiple economic and environmental advantages.

Text; Dataset

2021-06-09

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/78592

Mining Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/