Electrochemical Removal of Copper from Regenerated Pickling Solutions of Steel Plants
Bibliographic record
Abstract
The spent pickle liquor contains the dissolved metal salts of iron, chrome, copper, nickel and zinc [1]. The acid can be cleaned from its impurities, except for its copper ion, by the regeneration process. When the copper concentration of the pickling solution exceeds the level of about 100 ppm, the copper inside the solution replaces the iron in the steel and copper starts plating randomly on the metal surface in the following pickling processes. Because of the plating problem, the acid needs to be discard from the acid line. By adding fresh acid to the line the discarding can be delayed [2]. An electrochemical procedure was conducted to overcome the cementation problem experimentally in an environmental and economical way. Electrowinning of dilute copper from strong acid solution is a challenge in this case so the optimum current density, electrolysis duration and the electrode materials were determined in this study. It was found that increase in the current density and electrolysis duration increases the copper removal, but changes the morphology of deposits. A slower compact deposition procedure was selected to achieve continuous removal of copper instead of faster discontinuous powder deposition from the regenerated pickling solutions of steel plants. References [1] A. Agrawal, S. Kumari, and K. K. Sahu, “Iron and Copper Recovery/Removal from Industrial Wastes,” Metal Extraction and Forming DiVision, National Metallurgical Laboratory, India, 2009. [2] M. A. Nicholls, Z. Koont, B.D. Nelson, D. Bray and J. Felker (2008), Copper Plating Phenomenon During the Pickling of Steel. Pickling and Cold Rolling Department, ArcelorMittal Dofasco Inc., Hamilton, Ont., Canada.
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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 | 0.000 | 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".