Applications of AMT (Angle Measure Technique) in image analysis Part II: Prediction of powder functional properties and mixing components using, multivariate AMT regression (MAR)

This paper presents a novel way to predict the bulk powder characteristics from unilaterally illuminated powders using Angle Measure Technique (AMT) i mage analysis in combination with multivariate calibration. It is demonstra ted that the AMT transform can account for the complexity of images in the scale domain and be used as a strong preprocessing facility for multivariat e regression modeling. The concept of multivariate AMT regression has been proposed for this purpose. A wide variety of types of powders was collected in order to study the reliability, reproducibility and representativity of the methods. PLS models have been established to quantitatively predict ke y physical and behavioral powder properties such as particle size, density. minimum fluidization velocity, wall friction angle, and angle of repose. F inally, a first attempt at prediction of mixing component fractions in powd er mixtures has also been implemented, which can be used for on-line monito ring of many types of mixing process if fast digital imaging is available. (C) 2001 Elsevier Science B.V. All rights reserved.