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Mass Transfer

Mk-learning-python07 edited this page Sep 23, 2026 · 3 revisions

Mass transfer

Linear driving force throughout, with the overall coefficient built from resistances in series (Glueckauf approximation):

$$\frac{1}{K}=\underbrace{\frac{R_p}{3k_f}\frac{q^{\ast}}{c}}_{\text{film}}+\underbrace{\frac{R_p^2}{15\varepsilon_p D_p}\frac{q^{\ast}}{c}}_{\text{macropore}}+\underbrace{\frac{r_c^2}{15D_c}}_{\text{micropore}}$$

Five modes are selectable:

Mode What it uses
Macropore Macropore diffusion alone, optionally with Knudsen via the Bosanquet equation
Film External film alone, $k_f$ from Wakao & Funazkri (1978)
Micropore Intracrystalline diffusion alone, $D_c$ entered per component
Combined Any mix of film / macropore(+Knudsen) / micropore, ticked individually
Constant k An already-fitted LDF constant entered directly, bypassing every diffusivity estimate

The crystal radius $r_c$ is a property of the adsorbent, not of the gas, so a single value applies to both components; only the micropore diffusivity $D_c$ is entered per component.

The Reynolds number in the Wakao & Funazkri correlation — as in every packed-bed correlation in Ruthven Ch. 7 — is built on the superficial velocity, $Re=\rho,(\varepsilon v_0),d_p/\mu$. The correlation is stated for $3<Re<10^4$; outside that range the film coefficient is an extrapolation.

Molecular diffusivity is estimated by the Fuller–Schettler–Giddings correlation from the two gases selected, and previewed live in the sidebar. A Reality Check panel compares your $h_{in}$, $h_{out}$ and $k_z$ against three published correlations (Leva & Grummer 1948; Yagi & Kunii 1957; a Sieder–Tate-style external correlation) so fitted heat-transfer coefficients can be sanity-checked.

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