Wavelet method to film-pore diffusion model for methylene blue adsorption onto plant leaf powders

2012 ◽  
Vol 50 (10) ◽  
pp. 2775-2785 ◽  
Author(s):  
G. Hariharan ◽  
V. Ponnusami ◽  
R. Srikanth
1992 ◽  
Vol 9 (2) ◽  
pp. 109-120 ◽  
Author(s):  
Mohammad S. El-Geundi

The adsorption of basic dyestuffs (Basic Blue 69 and Basic Red 22) onto natural clay has been studied using a series of batch adsorption runs. The pore diffusion model (PDM) has been developed based on external mass transfer and pore diffusion to predict the performance of a batch adsorber. A computer program has been developed to generate theoretical Sherwood number-time curves and these results were adjusted to experimental Sherwood number-time curves by means of a ‘best fit’ approach. The variables of initial dye concentration and natural clay mass have been successfully correlated using a single external mass-transfer coefficient, Ks, and a single effective pore diffusion coefficient, Deff. The Ks values are 3.3 × 10−5 and 2.6 × 10−5 m/s for Basic Blue 69 and Basic Red 22, respectively. The Deff values are 7.3 × 10−10 and 9.6 × 10−10 m2/s for Basic Blue 69 and Basic Red 22, respectively.


Membranes ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 139
Author(s):  
Tuba Yaqoob ◽  
Muhammad Ahsan ◽  
Arshad Hussain ◽  
Iftikhar Ahmad

There is an enormous need in the health welfare sector to manufacture inexpensive dialyzer membranes with minimum dialysis duration. In order to optimize the dialysis cost and time, an in-depth analysis of the effect of dialyzer design and process parameters on toxins (ranging from tiny to large size molecules) clearance rate is required. Mathematical analysis and enhanced computational power of computers can translate the transport phenomena occurring inside the dialyzer while minimizing the development cost. In this paper, the steady-state mass transport in blood and dialysate compartment and across the membrane is investigated with convection-diffusion equations and tortuous pore diffusion model (TPDM), respectively. The two-dimensional, axisymmetric CFD model was simulated by using a solver based on the finite element method (COMSOL Multiphysics 5.4). The effect of design and process parameters is analyzed by solving model equations for varying values of design and process parameters. It is found that by introducing tortuosity in the pore diffusion model, the clearance rate of small size molecules increases, but the clearance rate of large size molecules is reduced. When the fiber aspect ratio (db/L) varies from 900 to 2300, the clearance rate increases 37.71% of its initial value. The results also show that when the pore diameter increases from 10 nm to 20 nm, the clearance rate of urea and glucose also increases by 2.09% and 7.93%, respectively, with tolerated transport of albumin molecules.


2008 ◽  
Vol 26 (9) ◽  
pp. 651-659 ◽  
Author(s):  
Elio E. Gonzo ◽  
Luis F. Gonzo

In this work, the film–pore diffusion model was applied to the adsorption of phenol onto peanut shell activated carbon in a batch stirred vessel. This two-resistance model was applied to predict the phenol concentration decay curves for different initial phenol concentrations, carbon particle sizes and dosages. The predicted concentration decay curves were compared with the experimental findings. The optimum best-fit values of the external mass-transfer coefficient and effective diffusion coefficients were found by minimizing the difference between the experimental and model-predicted phenol solution concentration. It was found that, under the experimental conditions employed in this study, the influence of the external mass-transfer resistance was low. A single value of the mass transport coefficient, kf, of (4.8 ± 1.3) × 10−3 (cm/s) described the whole range of system conditions. The difference between the corresponding values of the effective diffusivity, Deff, was not statistically significant. Consequently, a constant value of the effective pore diffusivity of (4.1 ± 0.4) × 10−6 (cm2/s) was sufficient to provide an accurate correlation of the decay concentration curve.


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