ONLINE FREE ACIDITY MEASUREMENT OF SOLUTIONS CONTAINING BASE METALS
Bibliographic record
Abstract
AbstractElectrodeless conductivity was employed to investigate the feasibility of continuous online monitoring of the free acidity of sulphate and/or chloride solutions of Cu and other base metals from various hydrometallurgical processes. These solutions were measured with both commercial and experimental electrodeless conductivity sensors from 15 to 250 °C. It was confirmed that hydrogen ion has the greatest impact on conductivity by carrying the bulk of the current because it is the most mobile ion. In combination with a solution speciation analysis the conductivity sensor can be calibrated to account for the contribution of Cu electrolytes as well as other dissolved salts allowing fast and accurate online measurement of free acid. The sensor was tested at both lab and pilot scale with very good performance. The average difference between the measured and the titrated acid concentration was less than 5%, which is excellent for industrial applications.On a employé la conductivité sans électrodes pour investiguer la faisabilité de surveillance continue en ligne de l’acidité libre de solutions de sulfate et/ou de chlorure de Cu et d’autres métaux de base de divers procédés d’hydrométallurgie. On a mesuré ces solutions avec des capteurs de conductivité sans électrodes tant commerciaux qu’expérimentaux dans la gamme de 15 à 250 °C. On a confirmé que l’ion hydrogène avait le plus grand impact sur la conductivité en transportant la majorité du courant parce que c’est l’ion le plus mobile. En combinaison avec une analyse de spéciation de solution, on peut calibrer le capteur de conductivité pour tenir compte de la contribution des électrolytes de Cu ainsi que d’autres sels dissous, permettant la mesure en ligne rapide et précise de l’acide libre. On a évalué le capteur tant au laboratoire qu’à l’échelle pilote avec un très bon rendement. La différence moyenne entre la concentration d’acide mesurée et titrée était de moins que 5%, ce qui est excellent pour les applications industrielles.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".