Improving the Sensitivity of Forward Light Scattering Technique to Large Particles

Abstract

The detection of a small number of large particles whose size lies outside the required size distribution is important in the processing of many materials when those particles result in the deterioration of the product quality. Laser diffraction, which is a very popular technique for on-line monitoring and process control, has, however, limited sensitivity to those few large particles. This paper deals with the improvement of this sensitivity. The¯uc-tuations of the signal received by the detectors were analyzed by both experiment and simulation. Two statistical approaches, crosscorrelation and principal component analysis (PCA), were applied. A detection procedure is proposed which is based on the combination of sweep selection through PCA and an appropriate deconvolution of the selected sweeps. The preliminary experiments with Al 2 O 3 powder showed an improved sensitivity to the large particles.

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

“…MSPC and PCA were chosen because of their evident applicability in this case and the encouraging earlier experiences of using these techniques for condition monitoring of an X-ray fluorescence analyzer [11,12]. There are also examples in the literature [13,14] where PCA has been used to detect abnormally large particles from single sweeps with laser diffraction. PCA and MSPC charts have been used to a monitor a batch cooling crystallization process in a similar approach to that presented in this current work [15].…”