Characteristics and Disposal Options of Sludges from an Oil Refinery Wastewater Treatment Plant in Iran, 2013
Bibliographic record
Abstract
Background & Aims of the Study: Industrial wastewater sludges must be disposed in a safe\nway because they have hazardous effects on the human and environment. The aim of this\nstudy is to investigate the physicochemical characteristics and disposal options of sludges\nfrom oil-water separator (OWS) and dissolved air flotation (DAF) clarifier of a Refinery\nwastewater treatment plant.\nMaterials & Methods: Sludge samples were collected in grab sampling manner, in 6\nmonth (April-September 2013) in order to be analyzed for their physicochemical\ncharacteristics. Kolmogorov-Smirnov Z, independent t-test, Mann-Whitney U test, one\nsample t-test and Wilcoxon signed rank test were used for statistical analysis. Canadian Soil\nQuality Guidelines (CSQG) and Florida Department of Environmental Protection Soil\nCleanup Target Levels (FDEPSCTLs) were used to discuss the disposal fate of the\ngenerated sludge.\nResults: As, Cd, Cu, Pb and Se were not detected in the studied sludge. As compared with\nCSQG, the investigated sludge were polluted for residential/parkland, agricultural,\ncommercial and industrial applications, because they contained high concentrations of Cr,\nNi and Zn. Also, according to FDEPSCTLs, the studied sludges were not suitable for\nresidential and non-residential applications due to their high Al and Ni contents. DAF\nsludge had a high Zn concentration for residential application, too.\nConclusions: present sludge management in the studied plant needs to be revised because\nmetals’ concentrations are above the international standards and guidelines.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".