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Improving the Robustness of Particle Size Analysis by Multivariate Statistical Process Control
Author(s) -
Mattila Marko,
Saloheimo Kari,
Koskinen Kari
Publication year - 2007
Publication title -
particle and particle systems characterization
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.877
H-Index - 56
eISSN - 1521-4117
pISSN - 0934-0866
DOI - 10.1002/ppsc.200701094
Subject(s) - robustness (evolution) , multivariate statistics , particle size , computer science , sample size determination , slurry , statistical analysis , data mining , statistics , mathematics , materials science , engineering , machine learning , chemistry , biochemistry , chemical engineering , composite material , gene
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.