Nanofiltration and Reverse Osmosis for Reuse of Indigo Dye Rinsing Waters
Bibliographic record
Abstract
A membrane based treatment strategy was developed for the possible recycling of rinsing wastewater from indigo dyeing to the process itself. Performances of three different nanofiltration (NF) (NF 270 and NF 90, Dow Film Tech, USA and NF 99, Alfa Laval, Denmark) and two different reverse osmosis (RO) (HR 98 PP and CA 995 PE, Alfa Laval, Denmark) membranes were investigated with wastewater collected from the first post-rinsing tank of indigo dyeing process of a denim manufacturing plant. Dead-end microfiltration with a 5 µm filter was employed to remove coarse particles and minimize fouling of further NF and RO membranes. For NF and RO, a lab scale plate-and-frame membrane module was operated at a pressure of 5.07 bar and at a 0.62 m/s cross-flow velocity. The permeate quality from all the tested NF and RO membranes was acceptable for reuse in terms of COD and color. However, only the permeate from HR 98 PP RO and NF 90 membranes were with an acceptable conductivity. On the other hand, NF 270 membrane was superior to all the other NF and RO membranes in terms of the permeation rate. Flux declines obtained for all membranes were higher than 50% but fouling was not considerable and completely reversible. The good performance of NF 270 in terms of permeate quality (permeate conductivity, color, and COD values were 4.3 mS/cm, 8 Pt-Co, and 87 mg/L, respectively) together with a higher flux makes this membrane preferable over the other membranes to recycle denim textile rinsing wastewaters.
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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) | 0.001 | 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 | 0.001 | 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".