Membrane-based treatment for tanning wastewatersA paper submitted to the Journal of Environmental Engineering and Science.
Bibliographic record
Abstract
Tanning wastewater was subjected to different unit operations to select the best treatment sequences. Textile membrane filtration (TMF), microfiltration (MF), and ultrafiltration (UF) were complemented by screening, flocculation or flotation operations. The general chemical characterization determined that the wastewater had a high organic load. The ecotoxicological study classified the wastewater as highly ecotoxic. The sequence of screening–TMF–UF was found to be the optimal treatment for wastewaters of the first and second soaking stages, while the sequence of screening–TMF–flotation–UF proved to be adequate for the liming wastewater concerning productivity and water quality. Larger pore sizes MF membrane or higher molecular weight cut off (MWCO) UF membranes with higher permeability to pure water showed lower permeation fluxes for tanning wastewater. After membrane treatments, a decrease in the ecotoxicity was measured. The use of membrane treatment technology showed to be promising in removing organic pollutants and allowing the reuse of water and chemicals in the process.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".