Sensor-based sorting opportunities for hydrothermal ore deposits: Raw material beneficiation in mining

Marinus Dalm

Research output: ThesisDissertation (TU Delft)

170 Downloads (Pure)


Sensor-based particle-by-particle sorting is a technique in which singular particles are mechanically separated on certain physical and/or chemical properties after determining these properties with a sensor. Sensor-based sorting machines can be incorporated into mineral processing operations in order to remove waste or sub-economic ore prior to conventional treatment. This has potential to reduce the consumption of energy and water during mineral processing and thereby decrease processing costs. Furthermore, sensor-based sorting can be used to separate different ore types in order to enhance control of the feed to mineral processing facilities and improve processing efficiency. For most ore types no sensors are known that can be used to detect the grade of ore particles. This is because many ores are polyminerallic rocks in which the economically important minerals occur in relatively low concentrations and in small grain sizes. However, the deposition of ore minerals during the formation of hydrothermal ore deposits is often related to specific hydrothermal alteration zones. This means that it might be possible to characterise the grade of such an ore by using sensors that are capable of detecting differences in hydrothermal alteration mineralogy. Sensors can be applied throughout the entire mining value chain to collect information on the characteristics of the mined ore in real-time. The information that sensors provide can be used to improve deposit models, improve ore quality control and optimise mineral processing. However, the applicability of real-time sensor technologies has not yet been assessed for many types of ore deposits. The aim of the study was to explore the opportunities and potential benefits of using sensors for real-time raw material characterisation in mining and investigate the opportunities for sensor-based particle-by-particle sorting at hydrothermal ore deposits. Investigating sorting opportunities was aimed at researching the applicability of real-time sensors to segment waste particles from ore particles and to distinguish between ore particles that represent different ore types. This is based on samples taken from the Los Bronces porphyry copper-molybdenum deposit, the Lagunas Norte epithermal gold-silver deposit, and the Cortez Hills carlin-style gold deposit. For all the deposits included in the study, a fraction of the waste could be segmented by using a Visible to Near-InfraRed (VNIR) and Short-Wavelength InfraRed (SWIR) spectral sensor to detect the hydrothermal alteration mineralogy. For Lagunas Norte and Cortez Hills, this sensor could also be used to distinguish between different ore types. The ability to segment waste was based on indirect relationships between certain alteration mineral assemblages and the copper or gold grade. Since these relationships correspond to the alteration-mineralisation relationships that generally occur at each deposit type, there is potential that sensors can also be used to segment waste at other porphyry, epithermal or carlin-style deposits. For all three deposits additional research is required to investigate whether it is economically feasible to use the discrimination capabilities of the VNIR-SWIR spectral sensor for sensorbased particle-by-particle sorting. The feasibility may be limited by surface contaminations of the ore particles feeding the sorter, the influence of water on the discrimination capabilities of the VNIR-SWIR sensor, and the sorting efficiency resulting from misclassification.
Original languageEnglish
Awarding Institution
  • Delft University of Technology
  • Jansen, J.D., Supervisor
  • Buxton, M.W.N., Advisor
Award date2 Nov 2018
Print ISBNs978-94-6186-946-3
Publication statusPublished - 2018


Dive into the research topics of 'Sensor-based sorting opportunities for hydrothermal ore deposits: Raw material beneficiation in mining'. Together they form a unique fingerprint.

Cite this