Effective separation factor depending on distillation degree and temperature

The possibility of plotting the dependence of the effective separation factor β (in a well-known refining equation) on the degree of distillation g and temperature (for given values of the vapor pressure of the evaporated substance, the Peclet number at the melting temperature, the activation energy of impurity diffusion and the initial separation factor β0) through building the dependence of the purity of the condensate on g at a given β0 and at values of the Peclet number corresponding to a number of considered temperatures is shown. An example of calculating this dependence for a model material based on beryllium is given. The patterns of the named dependence are noted.

Authors

Publication Details

Journal
Functional materials
Published
2025-03-23
DOI
https://doi.org/10.15407/fm32.01.157
Citations
2
Primary Topic
Process Optimization and Integration
Type
article
Field-Weighted Citation Impact
2.43
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Effective separation factor depending on distillation degree and temperature

A. I. Zhukov, A. I. Kravchenko
2 citations
Functional materials
Process Optimization and Integration
2.43
article

Effective separation factor depending on distillation degree and temperature

A. I. Zhukov, A. I. Kravchenko
article en
2 citations

Abstract

The possibility of plotting the dependence of the effective separation factor β (in a well-known refining equation) on the degree of distillation g and temperature (for given values of the vapor pressure of the evaporated substance, the Peclet number at the melting temperature, the activation energy of impurity diffusion and the initial separation factor β0) through building the dependence of the purity of the condensate on g at a given β0 and at values of the Peclet number corresponding to a number of considered temperatures is shown. An example of calculating this dependence for a model material based on beryllium is given. The patterns of the named dependence are noted.

Functional materialsVol. 32(1)
Openalex Percentile: Top 12%
Process Optimization and Integration
2.43
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.