Premium
Crystal Aggregation in a Flow Tube: Image‐Based Observation
Author(s) -
Borchert C.,
Sundmacher K.
Publication year - 2011
Publication title -
chemical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.403
H-Index - 81
eISSN - 1521-4125
pISSN - 0930-7516
DOI - 10.1002/ceat.201000465
Subject(s) - histogram , bivariate analysis , volume (thermodynamics) , kernel (algebra) , flow (mathematics) , biological system , crystal (programming language) , particle (ecology) , volumetric flow rate , population , materials science , mathematics , mineralogy , chemistry , computer science , statistics , image (mathematics) , artificial intelligence , mechanics , physics , thermodynamics , geometry , geology , combinatorics , oceanography , demography , sociology , biology , programming language
The aggregation of crystals within a flow tube was observed based on data extracted from images of the bypassing population. The experiments were conducted under different conditions, namely the flow rate and the particle concentration have been varied simultaneously and two different solvents were used in which the aggregation extent was found to be different under otherwise constant conditions. The analysis of images of bypassing crystals allows for the acquisition of rich datasets both in terms of the variety of shape descriptors and number of particles. This amount of data enables the determination of at least bivariate number distributions of high accuracy with simple histograms. The interpretation of the data is further improved with kernel histograms with which also higher‐dimensional volume distributions can be obtained in good quality based on relatively few data points. Indeed, the isosurfaces of 3D distributions turned out to be helpful for inspection of the acquired data.