Particle Size Distribution Values Research Articles

Zn–Ni sulfide selective precipitation: The role of supersaturation

The selective removal of Zn with Na 2S from a mixture of Zn and Ni was studied in a continuously stirred tank reactor. At pH 5 and pS 18 the selectivity was improved from 61% to 99% by reducing the supersaturation at the dosing points by means of the reduction of the influent concentrations. The particle size distribution (PSD) of the precipitates increased as the supersaturation decreased up to a mode of about 22 μm at pH 5. PSD values on the 100–200 μm range were obtained when the sulfide was dosed through a polytetrafluoroethylene (Teflon) membrane at low supersaturation. Zn precipitated as sphalerite, whereas Ni mainly formed amorphous particles with a pH dependent stoichiometry.

Some Physco-thermal properties of Rice Bran

Physical and thermal properties of a byproduct of rice (Oryza sativa) were carried out. This product is raw rice bran noted for its economic and medical and other industrial importance. The physical properties include: moisture content that ranged from 11.5 to 27.5 %(wb). Others are the bulk density, specific gravity and particle size. Some of these properties were combined for study on their effect on thermal conductivity which was one of the thermal properties studied. Mean values of the thermal conductivity determined ranged from 0.2456 to 0.5764 W/m oC depending on the moisture content of the raw rice bran and the variety. The two major varieties of Sippy-rice bran and Thai-rice bran were found not to be having differences in their mean values of the thermal properties especially when determined under same conditions. Statistically, the combined effect of particle size and moisture content were found also not be to be significant, apparently because the particle size distribution values were not distinct from each other. Keywords : Moisture content, Rice bran, thermal properties, Particle size, Thermal conductivity Global Journal of Pure and Applied Sciences Vol. 14 (3) 2008: pp. 319-324

Process optimization of preparing spherical titania colloids with uniform distribution using artificial neural networks

Optimal operating variables for preparing submicron uniform titania colloids were estimated using the artificial neural networks (ANN) modeling and the process optimization algorithms. Titania colloids were synthesized by a sol–gel method using mixture recipes of titanium tetraisopropoxide (TTIP), NH 3, and H 2O with ethanol/acetonitrile under temperature-controlled conditions. Different sets of the operating variables, such as [NH 3], [H 2O], and reaction temperature, were selected within an operating range to carry out Design of Experiment to evaluate the prepared particle size (PS) and the particle size distribution (PSD) data. The relationship between the operating variables and PS and PSD of the prepared samples can be constructed by an ANN modeling approach. The built ANN model was then used to predict PS and PSD values corresponding to the operating variables. The optimal operating conditions to fabricate different PS values with narrow PSD were determined by the ANN model with the optimization method. Meanwhile, the monodispersed colloids between 150 and 400 nm were fabricated using the determined optimal operating conditions.

Process optimization of preparing silica particles with uniform distribution in sub-micron sizes using artificial neural networks

Optimal concentrations of reactants for preparing sub-micron uniform silica particles were predicted using a process optimization algorithm. Silica particles were synthesized from a mixture of tetraethyl orthosilicate (TEOS), NH 3, and H 2O using a sol–gel reaction. Different sets of the reactant concentrations were selected within an operating range and were designed to evaluate the particle size (PS) and the particle size distribution (PSD) data. The relationship between the reactant concentrations and PS and PSD of the synthesized samples can be constructed by an artificial neural networks (ANN) modeling approach. The built ANN model was then used to predict PS and PSD values corresponding to the reactant concentrations. The optimization problem of determining a combination of the reactant concentrations to fabricate a certain PS with narrow PSD was solved by the ANN model and an optimization method. The optimal reactant concentrations were estimated and it showed that [TEOS] and [NH 3] needed to be controlled in smaller values; meanwhile, the produced particles between 200 and 500 nm can be obtained by fixing [TEOS] and [NH 3] and only adjusting [H 2O].

Size driven phase transition of barium titanate nanoparticles prepared by plasma chemical vapor deposition

Size driven phase transition of barium titanate nanoparticles prepared by plasma chemical vapor deposition

Use of neural networks in the analysis of particle size distribution by laser diffraction: tests with different particle systems

The application of forward light scattering methods for estimating the particle size distribution (PSD) is usually limited by the occurrence of multiple scattering, which affects the angular distribution of light in highly concentrated suspensions, thus resulting in false calculations by the conventionally adopted algorithms. In this paper, a previously proposed neural network-based method is tested with different particle systems, in order to evaluate its applicability. In the first step of the study, experiments were carried out with solid–liquid suspensions having different characteristics of particle shape and size distribution, under varying solid concentrations. The experimental results, consisting of the angular distribution of light intensity, particle shape and suspension concentration, were used as input data in the fitting of neural network models (NN) that replaced the optical model to provide the PSD. The reference values of particle shape and PSD for the NN fitting were based on image analysis. Comparisons between the PSD values computed by the NN model and the reference values indicate that the method can be used in monitoring the PSD of particles with different shapes in highly concentrated suspensions, thus extending the range of application of forward laser diffraction to a number of systems with industrial interest.

Iron Redistribution in Three Boralfs (Gray Luvisols) of Saskatchewan

AbstractThe transformation and translocation of Fe in three Boralfs (Gray Luvisols)3 were studied in relation to the Fe contained within silicate minerals and that existing in oxide forms. Quantitative evaluations of the Fe redistribution using a quartz index technique provided conclusive evidence that losses of Fe from E horizons and gains in Bt horizons are largely accounted for by the translocation of silicate‐Fe as a component of fine clays. The weathering of coarse clay to fine clay was documented; the fine clay fraction remained relatively unaffected. Some dissolution and movement of Fe in solution was also evident, particularly in the two profiles showing evidence of restricted drainage and periodic reduction. Detailed analyses of one of the soils studied indicated an 11% loss of total clay from the solum, involving a 16% loss of total Fe and a 19% loss of silicate‐Fe from the clay fractions. Most of the Fe lost from the clay was recovered in glaebules of sand and silt size, both as reprecipitated oxides and as occluded clay particles. The actual loss of clay from the solum was only about one‐half of the 11% loss indicated from particle size distribution values alone.