Project details

Project 1:

Simulating Prompt Gamma Neutron Activation Analysis (PGNAA) sensors to optimise sensor design and signal analysis enabling reduced data acquisition times.

Approach:

Monte Carlo simulation of PGNAA sensor for varying detector setups and ore types. Analysis of the detector response and sampling to aid sensor data analysis and optimisation of sensor design. Evaluation of the potential use of data from other sensors such as X-Ray Transmission (XRT) and hyperspectral imaging to aid analysis of PGNAA data.

Project 2:

Sensing variable mineralogy on high tonnage run-of-mine belts by combining data from multiple sensor types via data fusion.

Approach:

Analysis of numerous sensing techniques such as PGNAA, magnetic resonance imaging, XRT, x-ray fluorescence and hyperspectral imaging to identify beneficial combinations based upon sensing mechanisms. Machine learning analysis of potential sensor combinations will be performed on a combination of experimental observations and sensor simulations.

Image courtesy of Scantech – Elemental analysis using GEOSCAN

Dr Dylan Peukert
Email: dylan.peukert
@adelaide.edu.au

Publications

Peukert, D., Xu, C., Dowd, P., 2022. A Review of Sensor-Based Sorting in Mineral Processing: The Potential Benefits of Sensor Fusion. Minerals, 12(11), 1364. https://doi.org/10.3390/min12111364

Chen, J., Lu, T-F., Peukert, D., Dowd, P., 2023. A numerical sensitivity study – The effectiveness of RFID-based ore tracking through a simulated coarse ore stockpile and the impacts of key process variables. Powder Technology, 429, 118939. https://doi.org/10.1016/j.powtec.2023.118939

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