Recovery of Zn(II) and Ni(II) Binary from Wastewater Using Integrated Biosorption and Electrodeposition
Bibliographic record
Abstract
The present study aimed to obtain best operational conditions for biosorption of Zn(II) and Ni(II) binary metal solution in a fixed bed packed with wheat straw as biosorbent. The effects of bed depths, liquid flow rates and mixture metal concentrations on biosorption service time were investigated. The results showed that breakthrough service time of the biosorption columns (Cb = 2 mg/L Zn and Ni) increased with increasing bed depth, while decreased with increasing influent concentrations and flow rates, as expected. This paper further extended the study to investigate the competition of Zn(II) and Ni(II) binary in solution by performing biosorption tests at varied ratio of the metal concentrations. For biomass regeneration, the effect of desorbing agents (hydrochloric acid, nitric acid and sulphuric acid), their concentrations (0.1–0.5 mol/L) and flow rates (0.05–0.1 L/min) on recovery of Zn(II) and Ni(II) binary mixture was investigated. The best performance in desorption of Zn(II) and Ni(II) binary solutions were 0.1 mol/L H2SO4 and a 0.05 L/min inlet flow rate. Moreover, after five sorption/desorption cycles, the biosorbent still maintained its high adsorption capability. Electrodeposition was also used to recover metal ions from concentrated Zn(II) and Ni(II) binary solutions (about 340 mg/L) from the desorption step. It was found that the electrodeposition could reduce the metal concentrations down to wastewater discharge limit of 2 mg/L Zn and Ni ions.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".