Accuracy of size distributions obtained from single particle static digital image analysis

peer reviewe

Marble classification using scale spaces

peer reviewe

Grey level intercepts distributions and grain size estimation

peer reviewe

Sensitivity of particle size and shape parameters with respect to digitization

The growing success of image analysis based instruments for particle characterization demonstrates the importance of size and shape analysis in operations involving particulate materials. ISO norms for particle sizing using image analysis are being elaborated to clarify nomenclature and measurement principles. But despite this, there is still a lack of understanding of how the digital representation of a particle affects different size and shape parameters. It is the purpose of this paper to explore the magnitude of estimation errors of a series of size and shape parameters from different digital image representations of a single particle. These images are simulated from grey level images of black particles presenting a Gaussian transition towards their white background. Particles themselves are generated from analytical functions sampled by digital grids with variable densities, positions and orientations. Results of inscribed disk, elongation, circularity, roughness, roundness, etc. are plotted as a function of grid density (magnification) with error bars corresponding to the scattering of results for variable thresholds, grid translations and rotations As a conclusion, confidence intervals are given for parameters as a function of magnification and the most sensitive and robust methods of shape analysis are put forward

Robotic throwing controller for accelerating a recycling line

Recycling is a promising way to prevent the use of raw material and reduce energy consumption, air pollution and waste. However, the process of recycling has to be economically efficient in order to be adopted by industrial manufacturers. One way to achieve this goal is to improve the recycling rate. We propose a novel method to design a machine learningbased controller to improve the efficiency of a recycling line by throwing waste into buckets instead of picking and dropping them. Our proof-of-concept is demonstrated on stones, because of their simple and uniform shapes. The method enables an ABB IRB 340 robot to throw objects to buckets with an empirical success rate of 99%

Minéralogie des fines particules en pulpe en utilisant l'imagerie multispectrale

Quantitative mineralogy of fine particles in slurry from multispectral imaging Sophie Leroy, Godefroid Dislaire, Pierre Barnabé and Eric Pirard Mineral processing especially in its final stages relies heavily on the differential behavior of particles in pulps. In order to monitor hydrocyclones and flotation cells in almost real time, it is important to develop at line particle characterization providing information on particle distribution but also mineralogy. A sampling device has been elaborated based on a flow cell with variable wall spacing linked to a high pressure peristaltic pump. This setup allows for dispersion and dilution of the mineral slurry into the cell. Depending on the ore or gangue minerals to be controlled, particles can be imaged either in diffuse reflectance or in transmittance mode. A simple multispectral imaging module has been designed to acquire images at eight different wavelengths. The design is based on a series of dichroic filters thereby avoiding any moving part and enabling a very fast acquisition of multispectral images. Imaging artifacts due, among others, to specular reflectance from the glass window are minimized. Multispectral classification is used to outline particles appearing in the field of view and to qualify their main mineral component. In particular, areas reflecting a meaningful and discriminative spectrum are identified and compared to a multispectral database. The database is continuously enriched through testing of pure mineral particles under similar pulp conditions (dilution, grain size,…). Results of final classification are compared to the modal analysis obtained from polished blocks and conventional reflected light microscopy. The technology developed in this work sets the basis for at line monitoring of ore slurries with reasonably simple mineralogy. Extension of the spectral range is being considered for future developments

Optical analysis of particle size and chromite liberation from pulp samples of a UG2 ore regrinding circuit

Since the early seventies high chromite and low base metal sulphide (BMS) contents of the UG2 reef imposes technological challenges to mineral processors and extractive metallurgists. Forty years later, particle size distribution and size by size mineralogy are considered as key factors to the continuous improvement of the UG-2 ore metallurgy. With the successful development of ultra fine grinding technologies, a compromise has to be found between fine grinding to achieve platinum group minerals liberation and to avoid the overgrinding of gangue minerals, especially chromite gangue which is detrimental in smelting process. Indeed, fine chromite grains, despite of being naturally hydrophobic mineral, can be recovered in flotation concentrate by entrainment. In addition, overgrinding also increase liberation of naturally floatable gangue (talc) which contaminate the concentrate and need to be controlled during flotation. In this paper, innovative techniques of single particle image analysis from both dry and wet samples have been tentatively used to assess the chromite particle size distribution and the chromite grade of samples taken from a secondary milling circuit. Preliminary results demonstrate excellent potential for online particle imaging making use of both the particle geometry (size, shape) and the optical properties (translucency)

Conception et calibration d'un système d'acquisition hyperspectrale bi-cameras (VNIR et SWIR) pour la caractérisation d'alliages métalliques de l'industrie du recyclage

The conception of a prototype combining two hyperspectral cameras, one ranging from visible to near-infrared and the other covering short-wave infrared, is presented. The prototype aims at the characterization of millimeter-sized metallic alloys particles, originating from end-of-life vehicles and waste electrical and electronic equipment recycling. This paper is meant to serve as a support for a similar project by presenting difficulties encountered and available solutions. The calibration steps necessary to obtain quality reflectance data are also described. Classification results obtained on 100 metallic fragments dataset are finally presented.Iliad