Sieveless particle size distribution analysis of particulate materials through computer vision
- Mississippi State University (MSU)
- ORNL
This paper explores the inconsistency of length-based separation by mechanical sieving of particulate materials with standard sieves, which is the standard method of particle size distribution (PSD) analysis. We observed inconsistencies of length-based separation of particles using standard sieves with manual measurements, which showed deviations of 17 22 times. In addition, we have demonstrated the falling through effect of particles cannot be avoided irrespective of the wall thickness of the sieve. We proposed and utilized a computer vision with image processing as an alternative approach; wherein a user-coded Java ImageJ plugin was developed to evaluate PSD based on length of particles. A regular flatbed scanner acquired digital images of particulate material. The plugin determines particles lengths from Feret's diameter and width from pixel-march method, or minor axis, or the minimum dimension of bounding rectangle utilizing the digital images after assessing the particles area and shape (convex or nonconvex). The plugin also included the determination of several significant dimensions and PSD parameters. Test samples utilized were ground biomass obtained from the first thinning and mature stand of southern pine forest residues, oak hard wood, switchgrass, elephant grass, giant miscanthus, wheat straw, as well as Basmati rice. A sieveless PSD analysis method utilized the true separation of all particles into groups based on their distinct length (419 639 particles based on samples studied), with each group truly represented by their exact length. This approach ensured length-based separation without the inconsistencies observed with mechanical sieving. Image based sieve simulation (developed separately) indicated a significant effect (P < 0.05) on number of sieves used in PSD analysis, especially with non-uniform material such as ground biomass, and more than 50 equally spaced sieves were required to match the sieveless all distinct particles PSD analysis. Results substantiate that mechanical sieving, owing to handling limitations and inconsistent length-based separation of particles, is inadequate in determining the PSD of non-uniform particulate samples. The developed computer vision sieveless PSD analysis approach has the potential to replace the standard mechanical sieving. The plugin can be readily extended to model (e.g., Rosin Rammler) the PSD of materials, and mass-based analysis, while providing several advantages such as accuracy, speed, low cost, automated analysis, and reproducible results.
- Research Organization:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge National Environmental Research Park
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- DE-AC05-00OR22725
- OSTI ID:
- 1015063
- Journal Information:
- Computers and Electronics in Agriculture, Vol. 66, Issue 2; ISSN 0168-1699
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
ACCURACY
BIOMASS
COMPUTERS
DIMENSIONS
DISTRIBUTION
FORESTS
GRAMINEAE
IMAGE PROCESSING
JAVA
OAKS
PARTICLE SIZE
PARTICULATES
PINES
RESIDUES
RICE
STRAW
SWITCHGRASS
THICKNESS
WHEAT
WOOD
Biomass sieve analysis
Dimension
ImageJ plugin
Image processing
Particle size distribution
Physical property