Florent Bourgeois

Measurement of fracture energy during single-particle fracture

The population balance model provides a very good description of the comminution of brittle particulate material. The breakage function plays a central role in the population balance method and its variation with impact energy is now considered to be important when scaling up data for operating ball mills. The rate of breakage has been known for many years to impact-energy dependent. This paper describes the operation and calibration of a precise apparatus for the measurement of the distribution of fracture energies among particles and the relationship between the breakage function and the impact energy. The distribution of fracture energies was found to be log normal and varies consistently with particle size. The distribution of fracture energy for several different materials was determined. The one-to-one correspondence between the probability of breakage and the distribution of fracture energies is demonstrated.

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.

The CSIRO Hopkinson Bar Facility for large diameter particle breakage

The new CSIRO Hopkinson Bar Facility has been commissioned at the Queensland Centre for Advanced Technologies in Brisbane, Australia. Similar devices, such as the Ultra Fast Load Cell (UFLC) at the Utah Comminution Centre have proven to be extremely useful in providing fundamental breakage data of particulate material. In addition to its 19 mm and 60 mm diameter Hopkinson bars, the new CSIRO facility includes a large 100 mm Hopkinson Bar. This large diameter carbon steel load cell is instrumented with highly sensitive semi-conductor strain gauges that give it a force resolution of less than 100 Newtons, permitting precise measurement of the energy to fracture large mineral particles. The devices ability to measure breakage properties, such as first fracture force and energy, of large particles up 100 mm is unique. Breakage data for iron ores over a wide size range is presented. The well known comminution result of increasing particle strength and fracture energy with decreasing particle size was confirmed for the iron ores tested. The influence of iron ore mineralogy and texture was also noted.

Morphological analysis and modelling of fine coal filter cake microstructure

The microstructure of fine coal filter cakes governs their formation rate and dewaterability, which are both critical to the efficient beneficiation of fine coal. However, the principles by which the microstructure influences the transport properties of filter cakes are not well understood, partly due to the difficulty in obtaining information about the three-dimensional morphology of their pore structure. The paper describes an example of application of the Boolean model that leads to a straightforward and realistic quantification of the three-dimensional microstructure of fine coal filter cakes. Such a model is extremely attractive, for (1) it relies on well-established random set theory, (2) its characterisation involves standard image analysis of polished sections, and (3) no empiricism is involved. Although the model was found to be ideally suited to the characterisation of fine coal filter cakes, the flexibility of its underlying theory makes it an excellent candidate for quantifying other porous media. Such a model not only permits easy access to the three-dimensional details of porous media, but also holds promise for assisting in predicting the transport properties of porous media because of its realism. Subsequent publications will discuss applications of the model to obtain three-dimensional computer simulations of filter cakes, and to predict single-phase permeability of filter cakes.

Advances in the Fundamentals of Fine Coal Filtration

The fundamental principles governing the filtration of fine (nominally -0.5 mm) coal are not well understood. This has recently been recognised by the Australian coal industry as a major hurdle to significantly improving dewatering technology and performance. This study quantifies and compares the respective influences of filter cake microstructure, and coal surface and bulk properties on the vacuum filtration of fine bituminous coal slurries. It is shown that filter cake microstructure is the dominant factor, particularly in determining the kinetics of cake formation and desaturation. Although variations in coal properties probably have little effect on the filtration kinetics, they can significantly influence residual cake moisture. Moreover, the results obtained in this study indicate that there does not exist a single microstructure capable of providing both a high filtration rate and a low product moisture. Such a result is expected to influence future dewatering research efforts and possibly drive t...

Quantitative estimation of sampling uncertainties for mycotoxins in cereal shipments

Many countries receive shipments of bulk cereals from primary producers. There is a volume of work that is on-going that seeks to arrive at appropriate standards for the quality of the shipments and the means to assess the shipments as they are out-loaded. Of concern are mycotoxin and heavy metal levels, pesticide and herbicide residue levels, and contamination by genetically modified organisms (GMOs). As the ability to quantify these contaminants improves through improved analytical techniques, the sampling methodologies applied to the shipments must also keep pace to ensure that the uncertainties attached to the sampling procedures do not overwhelm the analytical uncertainties. There is a need to understand and quantify sampling uncertainties under varying conditions of contamination. The analysis required is statistical and is challenging as the nature of the distribution of contaminants within a shipment is not well understood; very limited data exist. Limited work has been undertaken to quantify the variability of the contaminant concentrations in the flow of grain coming from a ship and the impact that this has on the variance of sampling. Relatively recent work by Paoletti et al. in 2006 [Paoletti C, Heissenberger A, Mazzara M, Larcher S, Grazioli E, Corbisier P, Hess N, Berben G, Lübeck PS, De Loose M, et al. 2006. Kernel lot distribution assessment (KeLDA): a study on the distribution of GMO in large soybean shipments. Eur Food Res Tech. 224:129–139] provides some insight into the variation in GMO concentrations in soybeans on cargo out-turn. Paoletti et al. analysed the data using correlogram analysis with the objective of quantifying the sampling uncertainty (variance) that attaches to the final cargo analysis, but this is only one possible means of quantifying sampling uncertainty. It is possible that in many cases the levels of contamination passing the sampler on out-loading are essentially random, negating the value of variographic quantitation of the sampling variance. GMOs and mycotoxins appear to have a highly heterogeneous distribution in a cargo depending on how the ship was loaded (the grain may have come from more than one terminal and set of storage silos) and mycotoxin growth may have occurred in transit. This paper examines a statistical model based on random contamination that can be used to calculate the sampling uncertainty arising from primary sampling of a cargo; it deals with what is thought to be a worst-case scenario. The determination of the sampling variance is treated both analytically and by Monte Carlo simulation. The latter approach provides the entire sampling distribution and not just the sampling variance. The sampling procedure is based on rules provided by the Canadian Grain Commission (CGC) and the levels of contamination considered are those relating to allowable levels of ochratoxin A (OTA) in wheat. The results of the calculations indicate that at a loading rate of 1000 tonnes h−1, primary sample increment masses of 10.6 kg, a 2000-tonne lot and a primary composite sample mass of 1900 kg, the relative standard deviation (RSD) is about 1.05 (105%) and the distribution of the mycotoxin (MT) level in the primary composite samples is highly skewed. This result applies to a mean MT level of 2 ng g−1. The rate of false-negative results under these conditions is estimated to be 16.2%. The corresponding contamination is based on initial average concentrations of MT of 4000 ng g−1 within average spherical volumes of 0.3 m diameter, which are then diluted by a factor of 2 each time they pass through a handling stage; four stages of handling are assumed. The Monte Carlo calculations allow for variation in the initial volume of the MT-bearing grain, the average concentration and the dilution factor. The Monte Carlo studies seek to show the effect of variation in the sampling frequency while maintaining a primary composite sample mass of 1900 kg. The overall results are presented in terms of operational characteristic curves that relate only to the sampling uncertainties in the primary sampling of the grain. It is concluded that cross-stream sampling is intrinsically unsuited to sampling for mycotoxins and that better sampling methods and equipment are needed to control sampling uncertainties. At the same time, it is shown that some combination of cross-cutting sampling conditions may, for a given shipment mass and MT content, yield acceptable sampling performance.

Conditioning Circuit Analysis for Slimes Management in Quarries

Driven by increasingly stringent environmental regulations on water usage, many European quarries are in the process of adding thickening circuits to their wash plants for managing clayey tailings. One of the critical components of this circuit is slurry conditioning. With typical water consumption over 1 m 3 per ton of washed aggregates, quarries produce dilute slime streams that are conditioned with high flocculant dosages to maintain water clarification rates as high as possible. At present, conditioning systems used in the quarrying industry are designed from elementary rules-of-thumb derived from experience. Practitioners acknowledge that conditioning system design criteria should be investigated further in order to design more efficient full-scale conditioning systems. This work focuses on weir-based conditioning tanks used in European quarries. The paper presents an applied analysis of such conditioning systems based on a comparison between a full-scale conditioning circuit and a controlled laboratory conditioning set-up. Within the range of plant operating conditions, this approach shows that full-scale conditioning is essentially governed by average shear rate and conditioning time. However, fine floc size distribution measurements reveal that quarry slimes conditioning is a dynamic process. This explains the high sensitivity of the process to variations in hydrodynamics, and the challenges of industrial conditioning system design.