Recycling of Woven Fabric Dyeing Wastewater Practiced in Perundurai Common Effluent Treatment Plant
Bibliographic record
Abstract
Textile dyeing industries in Erode and Tirupur district of Tamilnadu (India) discharge effluents ranging between 100 and 200m³/t of production. Dyeing is performed by Jigger or advanced Soft Flow reactor process. Coloring of hosiery fabric takes place in the presence of high concentration of sodium sulphate or sodium chloride (30 – 75 kg/m³) in dye solutions. Wash water and dye bath waste water are the process effluents of dyeing industry which are collected separately and follow the advanced treatment for maximum recycling of recovered waters.Wash water is treated using a sequence of physicochemical and biological unit process, the waste water is passed into ultrafiltration (UF), two stages reverse osmosis (RO) membrane system where the permeate is reused for processes. The rejects about 10 – 12 % of the inlet volume is subject to reverse osmosis for sent to evaporators. Dye bath water after treating, the permeate is used in process for dye bath preparation and the reject of about 20 – 25% is sent to multi effect evaporator / solar evaporation pond (SEP). The final rejects from reverse osmosis system is directed to multi effect evaporator system where condensed waters are recovered. The removal of Total Dissolved Solids (TDS), Chemical Oxygen Demand (COD) and Chloride are in the range of 82 – 97%, 90 – 97% and 78 – 97% respectively. This study was carrier out Common Effluent Treatment Plant (CETP), Perundurai, SIPCOT, Erode district.
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 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 | 0.001 |
| Science and technology studies | 0.001 | 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".