Removal of Fe and Mn from Co leach solutions by adsorption on activated carbon based on post-consumer polyethylene terephthalate (PET) - Mechanism insights through linear and nonlinear isotherm and kinetic models
Bibliographic record
Abstract
The removal of Fe2+ and Mn2+ from cobalt sulfate-saturated solutions is frequently problematic for Cu-Co hydrometallurgical process performance. Among the applied and developed methods for the removal of these metals, adsorption has drawn less attention. This study investigates on the removal of Fe2+ and Mn2+ in industrial cobalt solutions known as raffinates by adsorption on activated carbon prepared from post-consumer polyethylene terephthalate (PET). The adsorption operating conditions were studied in synthetic solutions and industrial solutions. The Langmuir, Freundlich, and Temkin isotherms were then used to fit the adsorption data, while the pseudo-first-order (PFO), pseudo-second-order (PSO), and Elovich models were used to describe the kinetics. Linear and nonlinear kinetic and isotherm models were studied and compared. Furthermore, intraparticle diffusion (IPD) models were used to study the transfer mass mechanism. The results show that Fe2+ and Mn2+ are removed from Co solutions with the efficiencies of 62.14 and 68.43, respectively. The Langmuir model fits the Fe2+ and Mn2+ adsorption data better, indicating that chemisorption is preferred over physisorption, whereas the Freundlich model fits the Co2+ adsorption data better, indicating that physisorption is preferred over chemisorption. The PFO model describes well the kinetic behavior of Fe2+ and Mn2+, whereas the Elovich model describes better the kinetic behavior of Co2+. According to the IPD model, the step that limits the adsorption of Fe2+, Mn2+, and Co2+ is essentially diffusion through the activated carbon pores. The reported experimental results will highlight the use of activated carbon in the removal of Fe2+ and Mn2+ in Cu and Co hydrometallurgy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 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".