Production of Bioflocculant by Chryseomonas Luteola and Its Application in Dye Wastewater Treatment
Bibliographic record
Abstract
Inorganic aluminum salts as one of coagulation and flocculation reagent have been generally used for removing the impurities in water and waste water technology. In last a few years, was found that aluminium compounds have been reported as disease carrier for human being. In general, dyes wastewaters with concentrations in the range of 10 - 200 mg/L will be appearing at highly coloured. There are many reports which are mention about the toxic effects of dyes and metals in the form of carcinogenic, genetic effects. Furthermore, direct release of dyes wastewater into the environment is not encouraged due to the high values of un-degradable and degradable organics substances. In this study a bioflocculant was produced from palm oil mill effluent (POME) isolated and found as Chryseomonas luteola. The experiments conduct at optimized culture conditions (at temperature 50oC, duration 1 day), a flocculating activity of 96.15% was demonstrated using kaolin suspension. The result was also showed to be better for flocculation of a kaolin suspension over pH (5-7) and cations (Ca2+) will enhance the flocculating activity. The bioflocculant can be used for treating dye wastewater, and the maximal removal efficiency of turbidity and chemical oxygen demand (COD) were 38.22% and 33.25%, respectively at pH 7.0 with dosage of culture broth bacteria about 0.2 mL. Besides that, the reduction of turbidity and removal of COD of dye wastewater were conducted using aluminium sulphate (alum). A maximum reduction of turbidity was 97.51% while the removal of COD was 99.64%, were observed with a concentration of 600 mg/L at pH 4.0.
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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) | 0.001 | 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 | 0.001 | 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".