Selenium removal from waste waters by chemical reduction with chromous ions
Bibliographic record
Abstract
Selenium can be released to the environment from both natural and industrial activities, resulting in increased selenium concentrations in surface and ground water. Even small concentrations of selenium can be toxic for many forms of aquatic life. Therefore, selenium contamination in the receiving environment is a key issue for many industries. Selenium speciation in solution plays an important role in its removal. Selenite (SeO₃²⁻, Se(IV)) and selenate (SeO₄²⁻, Se(VI)) are the most important inorganic selenium species which are generally found in water and known to be toxic. Relative to the selenate, selenite can be quite easily removed from solutions using various treatment methods such as chemical reduction, precipitation and adsorption by ferrihydrite salts. However, these methods are not efficient for selenate removal. Typically, chemically based treatment processes for selenate removal require an initial reduction of selenate to the lower oxidation states (e.g., selenite, H₂Se). Chromous ions have been known as a powerful reducing agent in the reduction of many organic compounds, oxides, and sulphide minerals and in many proposed novel hydrometallurgical processes. Therefore, there is a high potential for chromous ions to reduce selenate effectively. In this study, the fundamental and practical aspects of the selenate reduction by chromous ions as a novel method to remove selenate from waste waters was investigated mainly in sulfate media. At first, the stoichiometry of selenate reduction by chromous ions was studied. Secondly, the kinetics of selenate reduction by chromous ions was studied over the wide range of acidity, chromous concentration, temperature and ionic strength. The reaction order with respect to the concentrations of selenate, chromous ions and hydrogen ions and the general rate law equation were determined. Furthermore, the effect of sulfate ions on the selenate reduction rate at different ionic strengths was studied. Thirdly, the reaction mechanism responsible for the reduction of selenate by chromous ions was suggested. Finally, the removal of hydrogen selenide generated from the reduction of selenate with chromous ions was studied using three reagents. Additionally, a hydrometallurgy flowsheet incorporating chromous generation, selenate reduction, hydrogen selenide removal, and chromic precipitation units was proposed.
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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) | 0.000 | 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".