Removal of Chromium and Nickel from Electroplating Wastewater Using Magnetite Particulate Adsorbent: (1) Effect of pH, Contact Time and Dosage, (2) Adsorption Isotherms and Kinetics
Bibliographic record
Abstract
Wastewater discharged into municipal sewer systems from electroplating process plants contains a heavy load of metal ions and often requires pre-discharge treatment. Treatment of wastewater to reduce the concentration of metal ions employing an adsorption process has been studied using a wide range of adsorbents. In this work, the concentrations of chromium and nickel ions in wastewater samples from a local electroplating shop were found to be above the limits set out by the Bulawayo City Council, and the Environmental Management Agency, a statutory agency under the Ministry of Environment and Tourism, Government of Zimbabwe. Furthermore, the removal of chromium and nickel ions from the wastewater using magnetite as an adsorbent is studied. Magnetite particulate adsorbent used in this experiment has demonstrated to be an effective adsorbent material. At the optimum process operating pH of 4 – 7 the absorbent was able to achieve removal rates of up to 99% for chromium and 98% for nickel. The adsorption processes for chromium and nickel have been proven to be physical in nature using the Dubinin-Radushkevich isotherm model. Also, the adsorption kinetics data fit well with pseudo second-order kinetic model.
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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) | 0.000 | 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) | 0.001 | 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 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".