Recovery and reuse of indigo dyeing wastewater using membrane technology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it