Filterability prediction of needle-like crystals based on particle size and shape distribution data

The isolation and further treatment of particles generated in a crystallization process is dependent on their size and shape. The work presented here analyzes the filtration performance of needle-like particles, which often exhibit long filtration times or high retention of mother liquor. The size and shape of populations of beta L-Glutamic Acid and gamma D-Mannitol particles are measured using an automated image analysis approach (as well as a standard light scattering method), and their associated cake resistance is determined in pressure filtration experiments. Using a partial least squares regression analysis we develop a model of the process and show that relative cake resistances can be predicted if the particle size distributions are accurately known. Furthermore, we show that the statistical model calibrated on a single compound (either of those used for this study), can be exploited to predict the relative cake resistances of another compound.