Application of recycling waste products for <i>ex situ</i> and <i>in situ</i> water treatment methods
Bibliographic record
Abstract
The specific objectives of the study were to determine the approximate design parameters for filter bed and highlight the possible scope of using mixed iron oxides rich smelter slag for in situ or ex situ treatment. The batch and column study was conducted to assess the As removal capacities from contaminated water. X-ray fluorescence (XRF) analysis of the slag waste product determined the presence of large quantities of iron (Fe). In this study, the maximum removal capacities were found to be approximately 1.78 mg As per g of slag and 100% removal of As(V) was achieved during the first 30 days of three column operations. The changes in redox potential (Eh) values and the changes in effluent pH throughout the column operation period indicated redox reactions occurring in the system. The column experiments were modelled using a semi-analytic solution to the advection–dispersion–adsorption equation incorporated in the commercial software, Pollute V7. From the best-fit of the modelling results to the experimental breakthrough curves, the hydrodynamic dispersion coefficient (D) was found to be 0.0115 and 0.00775 m2/day for column 1 and column 2, respectively, and 0.00862 m2/day for column 3. The values of the distribution coefficient (KD) were 0.18, 0.173 and 0.171 m3/kg or L/g for the three columns and 0.24 L/g from the batch test. The results from the experiments may be used to aid the design of a filter bed or reactive barrier in a scenario where the mixed iron oxides rich smelter waste product is used as a candidate reactive medium.
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 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".