Investigating the effect of operational and geometric parameters on the performance of an axial spiral series mixer in the solvent extraction process
Bibliographic record
Abstract
Abstract The performance of an axial spiral stirrer in mixing two aqueous and organic phases in a tank at different test conditions has been studied, and its effect on copper ion extraction was determined. The influence of speed, O/A, pH, and tank baffle parameters were investigated. Experiments were also done without a baffle, requiring a smaller tank diameter to study the effect of radial tip clearance without baffles. Mass transfer has been assessed by measuring the overall mass transfer coefficient and Sherwood number. Examination of the samples collected from nine locations on the stirring tank wall with the larger baffled tube diameter showed that apart from the speed of the stirrer, the extraction of Cu ions was influenced by the presence of baffle, liquid O/A ratio, and pH in that order. Cu ion extraction at optimal conditions (600 rpm, O/A = 1.2, pH = 2.5, and with baffle) at the end of the stirring time (180 s) was already at 99.5%. Moreover, the stirred flow axial concentration was uniform. The results of tests with a smaller diameter tank (i.e., without baffle) showed that the radial distance between this stirrer and the tank wall without the baffle after 360 s had only a 6.5% negative impact on Cu ion extraction effectiveness with the same optimal test conditions (600 rpm, O/A = 1.2, pH = 2.5), and an axially non‐uniform fluid concentration was observed. Also, the large value of Sherwood numbers acquired from all the experiments (in the 300 s) gave insight into the improved performance of this stirrer design compared to pure diffusion.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".