Prediction of filtration characteristics by multivariate data analysis

Antti Häkkinen*, Kati Pöllänen, Satu Pia Reinikainen, Marjatta Louhi-Kultanen, Lars Nyström

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

5 Sitaatiot (Scopus)


The behaviour of solid/liquid suspensions during filtration processes is strongly influenced by the properties of the particles and the liquid phase. Although prediction of common filtration characteristics, such as cake resistance, cake porosity and compressibility, has been studied extensively, general theoretical models that could be applied to complex real-life suspensions do not exist. Prediction of filtration characteristics has proved to be difficult even in those cases where an extensive set of experimentally obtained material data are available. This is due to the complexity of the cake formation process, which means that the number of influential variables that should be simultaneously considered in the models is large. Traditionally applied calculation and modelling techniques have been incapable of processing such large sets of input variables, which has consequently restricted the complexity of the models. This paper introduces an alternative procedure for predicting the filtration characteristics of solid/liquid suspensions from measured material properties. Empirical models were created using multilinear partial least squares regression (N-PLS) for the experimentally determined pressure filtration parameters and the particle size and shape data obtained by an automated image analyzer. The density and dynamic viscosity of the liquid phase were also included in the models as input variables. The filtration characteristics of the test suspensions were described by four different parameters and separate models were derived for each parameter. All four models were tested with an independent set of samples in order to validate the created models. The results presented in this paper show that the procedure can be applied to create models that enable filtration characteristics to be reliably correlated with particle size and shape distributions.

TilaJulkaistu - huhtikuuta 2008
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu


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