Thermal stability of crandallite CaAl3(PO4)2(OH)5.(H2O) A 'Cave' mineral from the Jenolan Caves

Thermogravimetry combined with evolved gas mass spectrometry has been used to characterise the mineral crandallite CaAl3(PO4)2(OH)5•(H2O) and to ascertain the thermal stability of this ‘cave’ mineral. X-ray diffraction proves the presence of the mineral and identifies the products after thermal decomposition. The mineral crandallite is formed through the reaction of calcite with bat guano. Thermal analysis shows that the mineral starts to decompose through dehydration at low temperatures at around 139°C while dehydroxylation occurs over the temperature range 200 to 700°C with loss of OH units. The critical temperature for OH loss is around 416°C and above this temperature the mineral structure is altered. Some minor loss of carbonate impurity occurs at 788°C. This study shows the mineral is unstable above 139°C. This temperature is well above the temperature in caves, which have a maximum temperature of 15°C. A chemical reaction for the synthesis of crandallite is offered and the mechanism for the thermal decomposition is given

Estimation of particle concentration profiles in a three-phase fluidized bed from experimental data and using the wake model

Particles with a size distribution in the range of 34 to 468 µm were fluidized in a three-phase bed using low liquid and gas velocities. Particle size distribution and pressure profile measurements were carried out at different locations in the bed in order to study the influence of fluid velocities on segregation and dispersion of particles in different size classes. The influence of gas velocity on particle mixing was analyzed in terms of internal solid fluxes, calculated by means of the wake model. Based on the experimental results, different particle distribution patterns were identified. Although no significant tendencies were observed for radial profiles, particles of different sizes have significantly different axial profiles, which are mainly affected by the velocity of the liquid phase. Thus, depending on the liquid velocity, smaller particles reach a maximum concentration at different bed heights

Neural network model for the on-line monitoring of a crystallization process

This paper presents the results of the application of a recently developed technique, based on Neural Networks (NN), in the recognition of angular distribution patterns of light scattered by particles in suspension, for the purpose of estimating concentration and crystal size distribution (CSD) in a precipitation process based on the addition of antisolvent (a model system consisting of sodium chloride, water and ethanol). In the first step, in NN model was fitted, using particles with different size distributions and concentrations. Then the model was used to monitor the process, thus enabling a fast and reliable estimation of supersaturation and CSD. Such information, which is difficult to obtain by any other means, can be used in the study of fundamental aspects of crystallization and precipitation processes