Pollutants of Wastewater Characteristics in Textile Industries
Bibliographic record
Abstract
Textile Industry is one of the most important and largest industrial sectors in Pakistan. It has a high importance in terms of its environment impact, since it consumes large quantity of textile industrial processed water and produces highly polluted discharge water. The textile industry uses high volume of water throughout its operation, from the washing of fibers to bleaching, mercerizing, dyeing, printing and washing of finished products. A process data collection was performed and integrated with a characterization of the process effluents in terms of treatability and reusability. In order to evaluate properly the wastewater loading, on analysis course was set. The samples were collected during four months period of time i.e. November, December, January and February 2009-2010 from the seven samples were collected from different textile mills and analyzed for various parameters such as Total Dissolved Solids(TDS), Chemical Oxygen Demand(COD), Biochemical Oxygen demand(BOD), pH, Electrical Conductivity(EC), and heavy metals like Cadmium(Cd), Chromium(Cr), Copper(Cu), Iron(Fe), Manganese(Mn), Nickel(Ni), Potassium(K), Phosphorous(P), Sodium(Na), Sulphur(S), Zinc(Zn) were found in within the limits. Concentrations of all these metal ions in the effluent were above the recommended NEQS. It was therefore concluded that textile effluents were highly polluted.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".