Studies on Flocculating Activity of Bioflocculant from Closed Drainage System (CDS) and Its Application in Reactive Dye Removal
Bibliographic record
Abstract
Technological production processes of organic dyes soluble in water, as well as the processes for their application intextile industries, may heavily pollute natural waters, particularly from the point of view of their pronounced coloredwastewaters. Reactive dyes are prominent among numerous groups of water-soluble dyes. The bioflocculant waseffective in flocculating a kind of reactive soluble dyes (Cibacron yellow FN_2R) in aqueous solution. A bioflocculant–producing bacterium were isolated from wastewater and sediments of Close Drainage Systems (CDS) located at the Praiindustrial area.. Compared with conventional chemical flocculants, bioflocculants are biodegradable and nontoxic, andproduce no secondary pollution. Sphingomonas paucimobilis was found to produce a bioflocculant with highflocculating activity for Kaolin suspension and water-soluble dyes. The best temperature flocculation performance was35°C and shaking speed of 160 rpm. The highest flocculating efficiency achieved for Kaolin suspension was 98.4% at35°C after 48 hours cultivation. Various culture temperatures were tested between 2 hours in order to investigate theireffect on the bioflocculant production when the culture temperature was 35°C which the flocculating activity ofSphingomonas paucimobilis was up to 98.4%. It was found that, flocculating rate depends on time and temperatures.Determination flocculating activity was shown Sphingomonas paucimobilis is biodegradable and increase in number ofbacteria during the time will confirm that. This study was conducted to biologically treat wastewater discharged fromthe textile industry using sequencing batch reactor (SBR) technology biological flocculation on COD removal andeffects of solids detention times and MLVSS on EPS production.
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) | 0.000 | 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) | 0.000 | 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".