Semi‐empirical model of brine evaporation rate in lithium processing
Bibliographic record
Abstract
Abstract Lithium is a critical element in the transition to cleaner energy and is produced primarily in the Lithium Triangle (Argentina, Chile, and Bolivia) through the evaporative process. This process involves brine concentration through solar and wind evaporation in large ponds, where the salts are concentrated and eventually reach their solubility product and crystallize. Dynamic brine evaporation is crucial to designing and optimizing evaporation ponds, where predicting the evaporation rate is essential. In this work, the evaporation of simple synthetic brines composed individually of NaCl, KCl, or MgCl2 was experimentally studied in an evaporation chamber that allows monitoring of air temperature, humidity, brine temperature, and air velocity. The results show that brines with the same initial ionic strength but of different nature have similar evaporation rates under the same evaporation conditions. The evaporation rate decreases as the ionic strength increases. During evaporation, the ionic strength and brine density increase due to the concentration of the salts but remain constant when crystallization begins. A semi‐empirical model was developed to correlate the evaporation rate of brines with their density, allowing this rate to be estimated with an error of less than 5% using easily measurable data. The model can be applied to natural brines from the lithium industry rich in NaCl, KCl, and MgCl2.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".