Bibliographic record
Abstract
During zinc electrowinning, fluoride is the primary cause of aluminum cathode corrosion and the onset of difficulty in stripping zinc from the aluminum blank. It is therefore of great interest to determine how to remove fluoride from zinc plant solutions to prevent cathode corrosion and difficult stripping. There has been recent work on the removal of fluoride from drinking water and ground water. Research on zinc sulfate electrolyte purification has focused on metallic impurity removal, for example Cu, Co,Ni, Cd, Ti while halide impurities have been briefly investigated. Therefore, the purpose of this work is to investigate a reliable and economical process for fluoride removal from zinc sulfate electrolyte. In this work, aluminum pre-loaded Lewatit® MonoPlus TP 260 resin was introduced as the absorbent. This absorbent has a high fluoride-loading capacity through the formation of aluminum-fluoride complexes on the resin structure. Also, both aluminum chloride and aluminum sulfate have been found to be effective sources of aluminum for pre-loading of the resin. The loaded aluminum along with co-loaded fluoride may be removed by sulfuric acid stripping. The resin can then be conditioned with sodium hydroxide prior to be re-loaded with aluminum. As for the results, when 10 ppm fluoride existed in the initial solution, the fluoride capacity of the aluminum pre-loaded resin was calculated as 7.4 g F/L resin. Additionally, the breakthrough point could achieve 1000 bed volumes. Therefore, the cycle of aluminum sulfate pre-loading, fluoride-loading, sulfuric acid stripping, sodium hydroxide regeneration was recommended for effective fluoride removal in the zinc sulfate system.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".