Influence of particle size and size distribution in deep bed filtration and dynamic microfiltration

Particle size and size distribution is an important parameter in solid liquid separation process especially in granular bed filtration and in dynamic microfiltration. This paper discusses their effects on the above processes from extensive experimental data obtained. In granular bed filtration, the experimental results showed that the initial efficiency follows the pattern reported by previous experimental and theoretical studies, i.e., lower efficiency for particles which fall in the range of critical size of 1 m. However, the particle removal during the transient stage increased with an increase in particle size for the range of sizes studied. An attempt was made to quantify these effects in granular bed filtration using semi-empirical approach. In dynamic membrane filtration also, the particle size plays a major role in the retention. However, despite the relative thickness of the membrane (compared to particle size) dynamic microfiltration appears more as a sieving process; the retention is mainly related to the largest pore size.

Surface coverage of filter medium in deep bed filtration : mathematical modelling and experiments

This paper highlights the importance of surface coverage in modeling the removal of particles in deep bed filtration. A model that considers the saturation of sites on which particle deposition occurs is used. Experimental results obtained with monodispersed suspensions of 0.46 and 0.816 μm latex particles at different influent concentrations and ionic strengths were used to calculate the fraction of filter grain surface (β1) on which actual particle deposition occurs. This will be useful in evaluating the filter performance in terms of the utilization of available surface area of the filter medium. Further, the level of dendrite formation of particles on filter grains during filtration is expressed in terms of β1 and the specific surface coverage, θT (the fraction of a filter grain surface that is covered by particles at time T, assuming that the filter grain is covered by a monolayer of particles). This can be used to compare the contribution of deposited particles in the removal efficiency of deep bed filtration for suspensions with different physical and chemical characteristics.

Mathematical modelling of deep bed filtration

Numerous mathematical models have been developed to evaluate both initial and transient stage removal efficiency of deep bed filters. Microscopic models either using trajectory analysis or convective-diffusion equations were used to compute the initial removal efficiency. These models predicted the removal efficiency under favorable filtration conditions quantitatively, but failed to predict the removal efficiency under unfavorable conditions. They underestimated the removal efficiency under unfavorable conditions. Thus, semi-empirical formulations were developed to compute initial removal efficiencies under unfavorable conditions. Also, correction for the adhesion of particles onto filter grains improved the results obtained for removal efficiency from the trajectory analysis. Macroscopic models were used to predict the transient stage removal efficiency of deep bed filters. O’Melia and Ali’s model assumed that the particle removal is due to filter grains as well as the particles that are already deposited onto the filter grain. Thus, semi-empirical models were used to predict the ripening of filtration. Several modifications were made to the model developed by O’Melia and Ali to predict the deterioration of particle removal during the transient stages of filtration. Models considering the removal of particles under favorable conditions and the accumulation of charges on the filter grains during the transient stages were also developed. This paper evaluates those models and their applicability under different operating conditions of filtration.

Deep bed filtration : mathematical models and observations

Numerous mathematical models have been developed to evaluate both initial and transient stage removal efficiency of deep bed filters. Microscopic models either using trajectory analysis or convective diffusion equations were used to compute the initial removal efficiency. These models predicted the removal efficiency under favorable filtration conditions quantitatively, but failed to predict the removal efficiency under unfavorable conditions. They underestimated the removal efficiency under unfavorable conditions. Thus, semi-empirical formulations were developed to compute initial removal efficiencies under unfavorable conditions. Also, correction for the adhesion of particles onto filter grains improved the results obtained for removal efficiency from the trajectory analysis. Macroscopic models were used to predict the transient stage removal efficiency of deep bed filters. The O’Melia and Ali1 model assumed that the particle removal is due to filter grains as well as the particles that are already deposited onto the filter grain. Thus, semi-empirical models were used to predict the ripening of filtration. Several modifications were made to the model developed by O’Melia and Ali to predict the deterioration of particle removal during the transient stages of filtration. Models considering the removal of particles under favorable conditions and the accumulation of charges on the filter grains during the transient stages were also developed. This article evaluates those models and their applicability under different operating conditions of filtration.

Muscle atrophy, pain, and damage in bed rest reduced by resistive (vibration) exercise

The purpose of this study was to investigate the effectiveness of a short-duration (5-6 min, 3 d·wk) resistive exercise program with (RVE) or without (RE) whole-body vibration in reducing muscle atrophy in the lower limb during prolonged inactivity when compared with that in an inactive control group. METHODS: As part of the second Berlin BedRest Study, 24 male subjects underwent 60 d of head-down tilt bed rest. Using magnetic resonance imaging, muscle volumes of the individual muscles of the lower limb were calculated before and at various intervals during and after bed rest. Pain levels and markers of muscle damage were also evaluated during and after bed rest. Adjustment of P values to guard against false positives was performed via the false discovery rate method. RESULTS: On the "intent-to-treat" analysis, RE reduced atrophy of the medial and lateral gastrocnemius, soleus, vasti, tibialis posterior, flexor hallucis longus, and flexor digitorum longus (P ≤ 0.045 vs control group) and RVE reduced atrophy of the medial and lateral gastrocnemius and tibialis posterior (P ≤ 0.044). Pain intensity reports after bed rest were lower in RE at the foot (P ≤ 0.033) and whole lower limb (P = 0.01) and in RVE at the thigh (P ≤ 0.041), lower leg (P ≤ 0.01), and whole lower limb (P ≤ 0.036). Increases in sarcomere-specific creatine kinase after bed rest were less in RE (P = 0.020) and RVE (P = 0.020). No differences between RE and RVE were observed. CONCLUSIONS: In conclusion, a short-duration RVE or RE can be effective in reducing the effect of prolonged bed rest on lower extremity muscle volume loss during bed rest and muscle damage and pain after bed rest. Copyright © 2014 by the American College of Sports Medicine.

Particle dynamics and heat transfer at workpiece surface in heat treatment fluidised beds

The particle behaviour is studied by the analysis of particle images taken with a high speed CCD digital video camera. The comparison of particle dynamics is performed for the fluidised beds without part, with single part and with multi-parts. The results show that there are significant differences in particle behaviours both in different beds and at different locations at part surfaces. The total and radiative heat transfer coefficients at different surfaces of a metallic component in a high temperature fluidised bed are measured by a heat transfer probe developed in the present work. The principle of the heat transfer probe is to measure the change in temperature of the heated metallic piece with time and, then, to extract the heat flux and heat transfer coefficients. The structure of the probe is optimized with numerical simulation of energy conservation for measuring the heat transfer coefficient of 150~600 W/m2 K. The relationship between the particle dynamics and the heat transfer is analysed to form the basis for future more rational designs of fluidised beds as well as for improved quality control.