Recovery and reuse of indigo dyeing wastewater using membrane technology
Bibliographic record
Abstract
The objective of this study is to develop a membrane-based generic treatment scheme for wastewaters of indigo dyeing process of denim industry, based on water reuse. For this purpose, firstly the performances of microfiltration (MF), coagulation, and ultrafiltration (UF) processes were evaluated as pretreatment alternatives and the best pretreatment alternatives appeared to be single stage 5 m MF and sequential 5 m MF followed by 100 kDa UF providing high permeation rate and high color retention. These two pretreatment alternatives were compared based on the performance of nanofiltration (NF) using NF 270 membrane, and the best pretreatment process was evaluated as 5 m MF that provided 87-92% color and 10% chemical oxygen demand (COD) retention. After the pretreatment tests, three different NF (NF 270, NF 90, Dow Filmtec, USA and NF 99, Alfa Laval, Denmark) and two different reverse osmosis (RO) membranes (HR 98 PP and CA 995 PE, Alfa Laval, Denmark) were tested to produce reusable water. Permeate COD and color performances of the tested NF and RO membranes were similar and satisfactory in meeting the relevant reuse criteria, while permeate conductivity was satisfactory only for HR 98 PP RO membrane and for NF 90 membrane. On the other hand, NF 270 membrane was superior to the other membranes in terms of permeation rate. For NF 270 membrane; cumulative color, COD and conductivity retentions were found to be 93 %, 92 %, and 60 %, respectively. When the developed process chain (5m MF+ NF 270) was also tested for a dilute indigo dyeing wastewater, it was found out that the developed scheme works similarly and is generic for indigo dyeing 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".