Computational analysis of citric acid pertraction in emulsion liquid membranes
Bibliographic record
Abstract
Abstract Citric acid is one of the most widely used acids in industry, and its recovery from waste streams is critical. Emulsion liquid membrane (ELM) is one of the most effective recovery methods that has been investigated in recent years. Numerous transport phenomena parameters affect the efficiency of this process. From the process equipment design point of view, optimization based on overall cost is of great importance, and important equipment sizing decisions/constraints must be considered. A physics‐based model for a full‐scale simulation of ELM systems is very useful. This work is focused on developing and verifying such a model. A coupled particle/mixture simulation was carried out in this work, and the modelling results were fitted on the experimental data. The novelty of this modelling work is physics‐based results based on the system's geometry and its effects on the mass transfer resistances. Since the model is physics‐based, the model is capable of simulating similar systems with any geometry or experimental conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".