A portable load cell for in-situ ore impact breakage testing

This paper discusses the design and characterisation of a short, and hence portable impact load cell for in-situ quantification of ore breakage properties under impact loading conditions. Much literature has been published in the past two decades about impact load cells for ore breakage testing. It has been conclusively shown that such machines yield significant quantitative energy-fragmentation information about industrial ores. However, documented load cells are all laboratory systems that are not adapted for in-situ testing due to their dimensions and operating requirements. The authors report on a new portable impact load cell designed specifically for in-situ testing. The load cell is 1.5 m in height and weighs 30 kg. Its physical and operating characteristics are detailed in the paper. This includes physical dimensions, calibration and signal deconvolution. Emphasis is placed on the deconvolution issue, which is significant for such a short load cell. Finally, it is conclusively shown that the short load cell is quantitatively as accurate as its larger laboratory analogues

Sampling with discrete contamination

The sampling variance for a process stream which carries fluctuating levels of the sought-after analyte and is subject to mass flow variation can be estimated from the covariance function of the analyte fluctuation and the covariance function of the mass flow when these covariance functions are well-defined and can be considered to be a stationary property of the process stream. However, in the case of sampling a flow of material (a one-dimensional lot) or from material removed from the hold of a ship (a three-dimensional lot) which does not possess a covariance function for the analyte of interest, a different approach must be taken. An important example of such a case is a shipment of grain that is contaminated by some component such as genetically modified organisms (GMOs) or by mycotoxins. Depending on the manner of contamination, the regions of the lot that carry contamination can be considered as randomly located distributions of concentration. The distributions themselves may be stochastic in that their mean concentrations and extents may be statistically defined rather than fixed. This paper develops the sampling variance for ‘slugs’ of contamination with a uniform concentration distribution and regular spacing of the sample increments, based on the assumption that the origins of the slugs are uniformly and randomly located (a Poisson point process)

Modelling multi-scale microstructures with combined Boolean random sets: A practical contribution

Boolean random sets are versatile tools to match morphological and topological properties of real structures of materials and particulate systems. Moreover, they can be combined in any number of ways to produce an even wider range of structures that cover a range of scales of microstructures through intersection and union. Based on well-established theory of Boolean random sets, this work provides scientists and engineers with simple and readily applicable results for matching combinations of Boolean random sets to observed microstructures. Once calibrated, such models yield straightforward three-dimensional simulation of materials, a powerful aid for investigating microstructure property relationships. Application of the proposed results to a real case situation yield convincing realisations of the observed microstructure in two and three dimensions

Fundamental understanding of swirling flow pattern in hydrocyclones

This work is concerned with establishing and validating a physics-based model that describes the swirling flow inside hydrocyclones. The physics is embedded in a Computational Fluid Dynamics (CFD) simulation model whose key features are presented and justified in the paper. Some features are selected in such a way that the model can eventually be used to simulate dense flow inside hydrocyclones. Nevertheless, its underlying physics is here within validated against dilute flow conditions. The model applies a Eulerian multi-fluid modelling approach for fluid–particle turbulent flows, and is computed using the semi-industrial code NEPTUNE_CFD. Simulation results are successfully compared to water split, velocity profiles inside the hydrocyclone and partition function measurements, either produced using our own experimental setup or from the literature. The work finds velocity profiles to be the most discriminating parameter for validation of the physics that describes the swirling flow inside the hydrocyclone. Water split on the other hand shows no relation to the choice of turbulence model and hence cannot be used to validate a mechanistic model of the hydrocyclone. The physics-based model presented here is the first building block towards describing and understanding hydrocyclone flow under dense regime

Use of OC curves in quality control with an example of sampling for mycotoxins

An ‘operating characteristics’ (OC) curve is a simple tool that has been in use in quality control for many years but does not seem to be widely applied in the particulate sampling field. The OC curve provides the probability that a lot of material will be deemed to meet a specification (will be found to have an assay that falls above (or below) a specified level, given the true assay of the lot). In the application considered herein, it provides the probability that a grain shipment will be accepted, given the true value of the assay for the lot. It directly measures the probability of a type II error. To construct the OC curve for a given sampling protocol, it is necessary to know all the relevant components of variance and their distribution, as a function of the level of contamination in the shipment. This may be quite a challenge in many circumstances as the assumption of normality of distributions may be poor when dealing with substances such as mycotoxins. The paper introduces the method of OC curve construction and reviews the method developed by Whitaker for the construction of OC curves for mycotoxins in a wide range of commodities. It is shown that his method excludes a potentially critical component of uncertainty. Further, the discussion concludes that the estimation of the distribution of the missing component of uncertainty is potentially prohibitively expensive and logistically very difficult. The final conclusion is that more intensive sampling methods should be employed for mycotoxins

Uncertainty

Uncertainty is a fact of life, and uncertainties are inevitable in science and engineering.Computational statistics brings analysis of real data at your fingertips, with uncertainties

Physical analysis and modeling of the Falcon concentrator for beneficiation of ultrafine particles

A predictive model of the Falcon enhanced gravity separator has been derived from a physical analysis of its separation principle, and validated against experimental data. After summarizing the previous works that led to this model and the hypotheses on which they rely, the model is extended to cover a wide range of operating conditions and particle properties. The most significant development presented here is the extension of the analytical law to concentrated suspensions, which makes it applicable to actual plant operating conditions. Two examples of industrial use cases are described and studied by interrogation of the model: dredged sediment waste reduction and coal recovery from fine tailings. Comparisons with empirical studies available in the literature show a good agreement between model predictions and industrial data. The model is then used to identify separation efficiency limitations as well as possible solutions to overcome them. These two examples serve to show how this predictive model can be used to obtain valuable information to improve physical separation processes using a Falcon concentrator, or to evaluate Falcon separator’s abilities for new applications

Beneficiation of concentrated ultrafine suspensions with a Falcon UF concentrator

Falcon concentrators are enhanced gravity separators designed for concentrating fine particles. The Falcon UF model is unique in that it is dedicated to beneficiation of ultrafines, one key feature being that it does not make use of any fluidization water. We investigated the physics of particle transport inside Falcon concentrators, and concluded that separation efficiency is governed by differential settling velocity. We derived and published a predictive model of the partition function under dilute conditions. We intend to extend the initial model to concentrated ultrafine suspensions for application to industrial scenarios by adding hindered settling to account for solid concentration effects

Experimental validation of a fluid dynamics based model of the UF Falcon concentrator in the ultrafine range

The process of separating ultrafine particles, say below 80 μm, on the basis of density is a true technical challenge. Indeed, the separation process itself becomes very much size dependent with such fine particles, so that large enough density differentials are necessary for offsetting the strong particle size effect. Our study is concerned with understanding the limitations of the UF Falcon concentrator, an enhanced gravity separator specifically designed for treating slurries with ultrafines. To this end, based on a number of hypotheses, we have already derived and published a theoretical model of the UF Falcon concentrator for treating dilute suspensions. This paper presents the validation and calibration of this model, based on experimental measurements carried out under controlled conditions using a laboratory scale concentrator. By comparing measured and predicted separation results for particles with known size distribution and density, the work validates the key model hypotheses, thereby confirming our understanding of the physics of the separation process. Moreover, by changing operating conditions in a systematic manner, the work is able to calibrate the model so that it can be used to make quantitative prediction of the UF Falcons performance

Fluid dynamics based modelling of the Falcon concentrator for ultrafine particle beneficiation

Enhanced gravity separators are widely used in minerals beneficiation, as their superior gravity field enables them to separate particles within narrow classes of density and size. This study aims to shed light on the Falcon concentrator’s ability to separate particles within size and density ranges lower than usual, say 5 to 60 μm and 1.2 to 3.0 s.g. respectively. As differential particle settling is expected to be the prevailing separation mechanism under such conditions, this study presents the workings of a predictive Falcon separation model that embeds phenomenological fluid and particle flow simulation inside the Falcon’s flowing film. Adding to the novelty of modelling the Falcon concentrator using a fluid mechanics approach, one point of practical significance within this work is the derivation of the Falcon’s partition function from fluid flow simulation results