Data · dataset · 2023
Computational code of square cascade for separation of Ne stable isotopes
Listed in ScienceDB
One of the methods for the separation of stable isotopes is the thermal diffusion column.
Description
The advantages of this method include small-scale operations because of apparatus simplicity and a small inventory, especially in gas phase operations. These features attract attention to the tritium and noble gas separation system.
In this research, the R cascade was used for designing and determining the number of columns. Moreover, the square cascade was adopted for the final design because of its flexibility. Calculations were performed as an example for the separation of 20Ne and 22Ne isotopes.
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Accordingly, all R cascades that have enriched Ne isotopes to more than 99% were investigated, and the number of columns was determined. Also, using the specified columns, the square cascade parameters were optimized. A calculation code entitled ''RSQ_CASCADE'' was developed in this regard.
The unit separation factor of 3 was considered, and the number of stages was studied in the range of 10 to 20. The results showed that the column separation power, the relative total flow rate, and the required columns were linearly related to the number of stages. The separation power and relative total flow decreased with the increase of stage number while the number of columns increased.
Therefore, the cascade of 85 columns was recommended to separate the Ne stable isotopes. These calculations resulted in the 17-stage square cascade, with five columns in each stage. By changing the stages cut, feed point, and cascade feed flow rate, the best parameters of square cascade were determined according to the cascade separation power and column separation power.
As the column separation power had the maximum value in the cascade feed 50, it was selected for separating Ne isotopes.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.07250 ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Geochemistry 73%
Provenance · 1 source records, 10 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.07250 | 10 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:group:3703 | enrichment · scidb cn | taxonomy-embedding@1.0.0 | title+keywords+description (73%) |
| concepts[field].local:field:earth-environmental | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:engineering | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:humanities | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:social-science | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license_text | source · scidb cn | connector:scidb_cn@1.0.0 | |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |