Data · dataset · 2025
Dataset and prediction model construction of melting enthalpy of binary molten salt systems and their performance research
Listed in ScienceDB
This study systematically collected experimental data on the melting enthalpy of binary molten salt systems, including chloride, nitrate, and carbonate salts.
Description
Based on this, four empirical prediction models were established, namely logarithmic form (Model I), linear additive form (Model II), fractional form (Model III), and power-law form (Model IV). Furthermore, the predicted values of each model were compared with the experimental values, and the errors were calculated.
The results showed that Model III, using a fractional form, exhibited the best prediction accuracy, with a total average error of only 0.07 between the predicted and experimental values. This indicates a fractional nonlinear relationship between the melting enthalpy of mixed molten salts and their different components. Model I had the second-highest prediction accuracy, while Models II and IV had larger prediction errors compared to the former two models.
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The prediction accuracy of each model for chloride and carbonate systems was higher than that for the nitrate system, which is believed to be related to the more complex spatial structure and stronger steric hindrance effect of nitrate ions. This study provides reliable models for the rapid prediction of melting enthalpy, a key thermal property of binary molten salts, and offers a reference and basis for the subsequent extension of prediction models to multi-component molten salt systems.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.j00265.00019 ↗
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
- Fluid mechanics and thermal engineering 69%
Provenance · 1 source records, 11 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.j00265.00019 | 11 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].anzsrc:group:4012 | enrichment · scidb cn | taxonomy-embedding@1.0.0 | title+keywords+description (69%) |
| 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 |