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Improving the Robustness of Particle Size Analysis by Multivariate Statistical Process Control

2007; Wiley; Volume: 24; Issue: 3 Linguagem: Inglês

10.1002/ppsc.200701094

1521-4117

Marko Mattila, Kari Saloheimo, Kari Koskinen,

Advanced Statistical Process Monitoring

Abstract The robustness of online particle size analysis in wet processes is improved by applying data based modeling methods to the control of the sample preparation and measurement sequence of the particle size analyzer. The aim is to find a more accurate and reliable method of determining the end of the particle size integration period using multivariate statistical process control (MSPC). The studied approach is tested on analyzers installed at two mineral processing plant sites and validated using two validation tests. Research shows that the proposed method works with two very different slurry types. The main advantage of the adapted approach is that there are no adjustable parameters that have to be set by the user.