Clarkson, Christopher Raymond
An experimental and modeling study was undertaken to determine the effect of coal composition, moisture content, and pore structure, upon natural gas adsorption and matrix transport. A volumetric high-pressure (up to 17 MPa) adsorption apparatus was constructed for the collection of single and multicomponent adsorption equilibrium and non-equilibrium adsorption data. A variety of equilibrium and non-equilibrium adsorption models were applied to determine which provided the best fits to the data. Coals selected for study included medium volatile bituminous coals of the Gates Formation, Northeastern B.C. Canada, and a suite of coals from the Sydney and Bowen Basins of Australia. Coal composition affects pore volume distribution, which in turn dictates the equilibrium and non-equilibrium adsorption characteristics of coals. Bright and banded bright coals have a greater amount of microporosity than dull coals, and hence have larger methane and carbon dioxide adsorption capacities. Dull coals have less microporosity but a greater amount of mesoporosity. Pore volume distributions in turn affect the adsorption rate behaviour of coals; bright coals have a uniform microporous structure and are adequately modeled using unipore diffusion models whereas dull and banded coals require models that account for a multimodal pore volume distribution. Coal composition also affects binary gas total adsorption isotherms, but has little effect upon carbon dioxide gas selectivity over methane. Coal moisture content appears to have a greater effect upon selective adsorption, but this requires further investigation. New numerical models, which account for bimodal pore volume distributions and non-linear adsorption characteristics, provide an adequate fit to adsorption rate data of the Gates coals. A bidisperse analytical model also provides excellent fits to the data, but does not account for non-linear adsorption. Models that do not account for non-linear adsorption yield optimized methane diffusivities that increase with pressure. The numerical model diffusivities decrease with an increase in pressure, possibly reflecting a bulk gaseous diffusion mechanism. Carbon dioxide diffusivities obtained from all models are larger than methane diffusivities. Methane diffusivities obtained using moisture-equilibrated coal data are smaller than those determined for dry coal. The Dubinin-Astakhov and Dubinin-Radushkevich isotherm equations provide better fits than the Langmuir equation to equilibrium methane and carbon dioxide adsorption data. The Dubinin models, which are based upon pore volume filling/adsorption potential theory, also have general validity in their application to supercritical methane-coal systems. Binary gas equilibrium predictions vary depending on whether the IAS or extended Langmuir model is used. The IAS theory, used in conjunction with the Dubinin-Astakhov equation, provides the best fit to CH₄/CO₂ adsorption data collected during this study.
E.Hamzeloo; M.Massinaei; N.Mehrshad
Monitoring and controlling particle size distribution in crushing and grinding circuits are essential for improved energy efficiency and metallurgical performance. Machine vision is probably the most suitable approach for on-line particle size estimation because it is robust, cost-effective and non-intrusive. In the present study, size distribution of particles in crushing circuit of a copper concentrator was estimated using image processing and neural network techniques. Several images were taken from material on a conveyor belt and processed for particle identification and segmentation. A number of the most commonly used size features were extracted from the segmented images and their potential to estimate the actual particle size, represented by sieve size analysis, was evaluated. The results showed that there were substantial differences between size distributions obtained from various size measures. Maximum inscribed disk was found to be the most effective feature for particle size description. Finally, the particle size distribution of material on the conveyor belt was precisely estimated by Principal Component Analysis (PCA) and neural network techniques. The proposed soft sensors can be used for real time measurement of particle size distribution in the industrial operations instead of sophisticated and expensive instruments.
Particle size distribution; Sieving; Image processing; Neural network
G.H.A. Janaka J. Kumara, Kimitoshi Hayano and Keita Ogiwara
Particle size distribution of granular materials is usually evaluated by sieve analysis test. In this research, an image analysis technique using ImageJ is proposed to evaluate particle size distribution of gravels. On particular conditions, some differences of gradation curves determined by sieve analysis and image analysis were observed. Based on the results, several aspects related to image analyzing are discussed in the paper. They include appropriate evaluation of particle grain size in image analysis, minimization of shadow effects appeared in images, effects of number of particles adopted for sieve analysis and image analysis and so on. It was found that grain size in image analysis should be defined appropriately to compare the gradation curves by the two methods. Probably, due to light effects, it was also observed that black color sheets are better than white color sheets to place particles. This method can be used as an in-situ test method since this method needs only a camera and a computer.
Coarse material, ImageJ, Image analysis, Particle size distribution, Sieve analysis