Treatment of biosorption and ozonation on elimination of the day pollution in treated water
Bibliographic record
Abstract
Textile wastewater is characterized by strong color, saturation of unfixed dyes and their residues, and organic and inorganic chemicals, such as bleaches, detergents, optical brighteners, fixing, sequestering and finishing agents, carriers, surfactants, equalizers, It is characterized by consumption of high amount of water and production of large volumes of wastewater from various processes, such as dyeing, printing, finishing, The wastewater treatment methods are broadly categorized as physical, chemical and biological. The integrated processes including some combinations of three treatment classes are mostly employed to remove color from dye contaminated effluents and comply with severe regulations. This review revealed that a combination of biosorption followed by ozonation might more efficient in terms of time, cost and toxicity reduction than separately using these processes. To compensate the negative points of both the processes, combined treatment of biosorption followed by subsequent ozonation have been implied. The treatment of ozonation has been increasing for the decolorization of textile dyes, the main disadvantage of this way of apply being related to the by products, which can have toxic and carcinogenic actions. The other disadvantage related to ozonation is its cost and time taken by ozonation. On the other hand, biosorption is a cheap process for the treatment of textile dyes, but it is less efficient in removing dyes as compared to ozonation at higher dye concentrations. It can be suggested that a combination of biosorption followed by ozonation might more efficient and effective for the decolorization of textile dye and to reduce the toxicity of ozonation process. © 2020, Green Wave Publishing of Canada. All rights reserved.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".