Enhancing Mining Quality Control Precision: Discover Aeris at Your Desk

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Join us to explore the critical role of quality and process control throughout every facet of mining and mineral processing. From the initial stages of ore exploration to the refinement of the final product, precision is paramount. Traditionally, material characterization in mining operations has relied heavily on chemical methods such as X-ray fluorescence (XRF) spectrometry or labour-intensive wet chemical analysis. Unfortunately, the mineralogical aspects defining both physical and chemical properties were often overlooked due to time constraints or complexities in data interpretation. Fortunately, the landscape is evolving. With the advent of rapid X-ray diffraction (XRD) platforms and advanced detectors, coupled with automated sample preparation and analysis, mineral quantification has surged to the forefront of process monitoring and quality control.

In this demo at your desk, we invite you to discover the Aeris compact system. Witness firsthand its intuitive operation and robust functionality through a series of real-world applications drawn from the mining industry. With its ability to deliver superior data quality in record time, alongside automated Rietveld full pattern fitting and Partial Least Square Regression analysis (PLSR), the Aeris is poised to revolutionize quality and process control environments.


  • Matteo Pernechele - Application Specialist XRD, Malvern Panalytical
  • Nicholas Norberg - Team Leader Development Scientists XRD NPD, Malvern Panalytical

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Who should attend? 

  • Professionals working for mining and minerals companies.
  • Researchers, professors and technical associations searching for knowledge and new mining analysis solutions.

What will you learn? 

  • Unveil the user-friendly features of the Aeris benchtop X-ray diffractometer through the demonstration. 
  • Discuss the real-world case studies from the mining and minerals industry to learn the ins and outs of performing measurements, executing phase identification, quantification, and harnessing XRD raw data for predictive process parameter analysis.