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2.4 DIFFUSION COEFFICIENT: MEASUREMENT AND PREDICTION
The proportionality factor of Fick’s law is called diffusivity or diffusion coefficient which can be defined as the ratio of the flux to its concentration gradient and its unit is m2/s. It is a function of the temperature, pressure, nature and concentration of other constituents. Diffusivity decreases with increase in pressure ( DAB ∝1/ p for moderate ranges of pressures, upto 25 atm) because number of collisions between species is less at lower pressure. But the diffusivity is hardly dependent on pressure in case of liquid. The diffusivity increases with increase in temperature ( DAB ∝T1.5 ) because random thermal movement of molecules increases with increase in temperature. The diffusivity is generally higher for gases (in the range of 0.5×10–5 to 1.0 × 10-5 m2/s) than for liquids (in the range of 10–10 to 10-9 m2/s). The diffusivity value reported for solids is higher in the range of 10–13 to 10-5 m2/s. Diffusion is almost impossible in solids because the particles are too closely packed and strongly held together with no ‘empty space’ for particles to move through. Solids diffuse much slower than liquids because intermolecular forces in solid are stronger enough to hold the solid molecules together.

2.4.1 Measurement of gas-phase diffusion coefficient 

There are several methods of experimental determination of gas-phase diffusion coefficient. Two methods are (a) Twin-bulb method and (b) Stefan tube method. Predictive Equations are sometimes used to determine diffusivity. These may be empirical, theoretical or semi-empirical.

(a) Twin-bulb method 
Two large bulbs are connected by a narrow tube. The schematic representation is shown in Figure 2.6. In the beginning two bulbs are evacuated and all the three valves
[V1, V2 and V3] are kept closed. Then V2 is opened and bulb 1 is filled with pure A at a pressure P. After that V3 is opened and bulb 2 is filled with pure B at the same pressure P. At steady state
Diffusion Coefficient: Measurement And Prediction | Mass Transfer - Chemical Engineering                            (2.37)
where, a is cross sectional area of the connecting tube of length l. If pA1 and pA2 are partial pressures of A in two bulbs at any time,
Diffusion Coefficient: Measurement And Prediction | Mass Transfer - Chemical Engineering                                                 (2.38)
and   Diffusion Coefficient: Measurement And Prediction | Mass Transfer - Chemical Engineering                                           (2.39)

From Equations (2.38) and (2.39) we have
Diffusion Coefficient: Measurement And Prediction | Mass Transfer - Chemical Engineering                     (2.40)
  Diffusion Coefficient: Measurement And Prediction | Mass Transfer - Chemical Engineering       (2.41)
Boundary conditions: 
Diffusion Coefficient: Measurement And Prediction | Mass Transfer - Chemical Engineering
applying the above boundary conditions, Equation (2.41) is integrated to obtain the expression of DAB as follows:
Diffusion Coefficient: Measurement And Prediction | Mass Transfer - Chemical Engineering                          (2.42)


Diffusion Coefficient: Measurement And Prediction | Mass Transfer - Chemical Engineering
Figure 2.6: A schematic of the twin bulb apparatus

(b) Stefan tube method 
Stefan tube consists of a T tube made of glass, placed in a constant temperature water bath. Air pump is used to supply the air, passed through the T tube as shown in Figure 2.7. Volatile component is filled in the T tube and air passed over it by the pump and change in the level is observed by the sliding microscope. Let, at any time t, partial pressure of A at the Z distance from the top of the vertical tube is pA1 and that at the top it is pA2. The diffusional flux of A is given as:
Diffusion Coefficient: Measurement And Prediction | Mass Transfer - Chemical Engineering                           (2.43)
The rate of evaporation is given by
Diffusion Coefficient: Measurement And Prediction | Mass Transfer - Chemical Engineering                      (2.44)

Boundary conditions:
t=0; Z=Z0 
t=t; Z=Z/
Integration of Equation (2.44) using the above boundary conditions gives, 
Diffusion Coefficient: Measurement And Prediction | Mass Transfer - Chemical Engineering                                 (2.45)
where, partial pressure of a at liquid surface, pA1 is equal to vapor pressure at the same temperature. The partial pressure of A at the top of the vertical tube, pA2 is zero due to high flow rate of B.
Diffusion Coefficient: Measurement And Prediction | Mass Transfer - Chemical Engineering
Figure 2.7: A representation of the Stafan tube

(c)Predictisve Equations:
(I) Empirical: Fuller, Schettler and Giddings
Diffusion Coefficient: Measurement And Prediction | Mass Transfer - Chemical Engineering                   (2.46)
where, T is temperature in K
MA, MB are molecular weights of A and B
P is total pressure in bar
νA, νA are atomic diffusion volume in m3 .

(II) Theoretical: Chapman-Enskog Equation
Diffusion Coefficient: Measurement And Prediction | Mass Transfer - Chemical Engineering                                      (2.47)
where, σAB is characteristic length parameter of binary mixture in Å, ΩD is collision integral=f(kT/εAB)
Diffusion Coefficient: Measurement And Prediction | Mass Transfer - Chemical Engineering                               (2.48)

The document Diffusion Coefficient: Measurement And Prediction | Mass Transfer - Chemical Engineering is a part of the Chemical Engineering Course Mass Transfer.
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FAQs on Diffusion Coefficient: Measurement And Prediction - Mass Transfer - Chemical Engineering

1. What is the diffusion coefficient and why is it important in chemical engineering?
The diffusion coefficient is a measure of how quickly a substance can move or spread through another substance by random molecular motion. In chemical engineering, it is important because it helps determine the rate at which mass transfer occurs, which is crucial in many processes such as separation, reaction, and adsorption.
2. How is the diffusion coefficient measured in chemical engineering?
The diffusion coefficient can be measured experimentally using various techniques. One common method is the "steady-state diffusion cell" approach, where a concentration gradient is established across a membrane or barrier, and the diffusion flux is measured. Another method is the "pulse-gradient" technique, where a pulse of the diffusing substance is introduced into a stagnant fluid and the resulting concentration profile is measured over time.
3. Can the diffusion coefficient be accurately predicted in chemical engineering?
While experimental measurement is often necessary for accurate determination of the diffusion coefficient, prediction methods can also provide valuable estimates. These prediction methods are based on various theoretical models, such as the Stokes-Einstein equation for simple liquids or the Maxwell-Stefan equations for multicomponent systems. However, the accuracy of these predictions depends on the complexity of the system and the assumptions made in the models.
4. What factors can affect the diffusion coefficient in chemical engineering processes?
Several factors can influence the diffusion coefficient in chemical engineering processes. These include temperature, pressure, concentration gradients, molecular size and shape, viscosity of the medium, and the presence of other substances that may interact with the diffusing species. Additionally, the diffusion coefficient can also be affected by external factors such as the presence of a membrane or barrier that introduces resistance to the diffusion process.
5. How can the diffusion coefficient be optimized in chemical engineering applications?
Optimizing the diffusion coefficient in chemical engineering applications often involves manipulating various factors to enhance mass transfer efficiency. This can be achieved by adjusting temperature and pressure conditions, optimizing concentration gradients, selecting appropriate materials or membranes with favorable diffusion properties, and considering the use of catalysts or additives to enhance diffusion rates. Computational modeling and simulation techniques can also be used to predict and optimize diffusion coefficients in complex systems.
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