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) image analysis in combination with multivariate calibration. It is demonstrated that the AMT transform can account for the complexity of images in the scale domain and be used as a strong preprocessing facility for multivariate 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 key physical and behavioral powder properties such as particle size, density, minimum fluidization velocity, wall friction angle, and angle of repose. Finally, a first attempt at prediction of mixing component fractions in powder mixtures has also been implemented, which can be used for on-line monitoring of many types of mixing process if fast digital imaging is available.