Quality and process control are crucial elements during all stages of mining and mineral processing from initial ore exploration, grading, blending, and beneficiation to the final product. In the past material characterization for control purposes in most mining operations relied on chemical methods like X-ray fluorescence (XRF) spectrometry or time-consuming wet chemical analysis. In contrast, the mineralogy that defines the physical and chemical properties was only monitored infrequently or even neglected, partially due to time limitation or difficulties in data interpretation. However, direct monitoring of sample mineralogy and process parameters does make the difference in describing ore bodies and the efficiency of the downstream beneficiation process.
With the emergence of faster X-ray diffraction (XRD) platforms and detectors in combination with partly or fully automated sample preparation and analysis routines, mineral quantification has been increasingly used for process monitoring and quality control over the past decade.
During this live demonstration we will introduce you to the Aeris benchtop system and demonstrate its ease of use and functionality using several application examples from the mining industry. The superior data quality that can be reached in very short measurement times in combination with fully automated Rietveld full pattern fitting and Partial least square regression analysis (PLSR) allows the Aeris to be operated in quality and process control environments.
발표자
Nicholas Norberg - Application Specialist XRD
자세한 내용
- Who should attend?
Professionals working for mining, minerals and metals companies, researchers, professors and technical associations searching for knowledge and new analysis solutions.
- What will you learn?
In this webinar a live demonstration will educate you on the ease of use for the benchtop X-ray diffractometer, Aeris. Several case studies from the mining and minerals industry will educate you on how to perform a measurement, execute phase identification, quantification and the use of the XRD raw data for the prediction of process parameters.