The last 25 years of research on bioflocculants for kaolin flocculation with recent trends and technical challenges for the future
Bibliographic record
Abstract
The generation of kaolin-containing wastewater is an inevitable consequence in a number of industries including mining, wastewater treatment, and bitumen processing. In some cases, the production of kaolin tailings waste during the production of bitumen or phosphate is as high as 3 times greater than the actual produced product. The existing inventory of nearly five billion barrels of oil sands tailings alone represents a massive storage and reclamation challenge, as well as a significant economic and environmental liability. Current reclamation options like inorganic coagulants and organic synthetic polymers may settle kaolin effectively, but may themselves pose an additional environmental hazard. Bioflocculants are an emerging alternative, given the inherent safety and biodegradability of their bio-based compositions. This review summarizes the different research attempts towards a better bioflocculant of kaolin, with a focus on the bioflocculant source, composition, and effective flocculating conditions. Bacillus bacteria were the most prevalent single species for bioflocculant production, with wastewater also hosting a large number of bioflocculant-producing microorganisms while serving as an inexpensive nutrient. Effective kaolin flocculation could be obtained over a broad range of pH values (1–12) and temperatures (5–95°C). Uronic acid and glutamic acid were predominant sugars and amino acids, respectively, in a number of effective bioflocculants, potentially due to their structural and charge similarities to effective synthetic polymers like polyacrylamide. Overall, these results demonstrate that bioflocculants can be produced from a wide range of microorganisms, can be composed of polysaccharides, protein or glycoproteins and can serve as effective treatment options for kaolin. In some cases, the next obstacle to their wide-spread application is scaling to industrially relevant volumes and their deployment strategies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".