Aerobic granulation for wastewater bioremediation: A review
Bibliographic record
Abstract
Abstract Rapid industrialisation and urbanisation releases numerous toxic compounds into natural water bodies, polluting these pristine fresh water resources. This is a subject of great concern, and the attention of environmentalists around the world has been increased towards this problem in recent years. Several techniques have been proposed for efficient wastewater treatment, most of them presenting some limitations, such as poor capacity, the generation of waste products, incomplete mineralisation and a high operating cost. Currently, aerobic granulation treatments are considered to be the most effective and economic alternative. Aerobic granulation is a process of microbial self‐immobilisation that results into a cell‐structured shape, characterised by dense biomass. Aerobic granules have a number of advantages over conventional bioflocs, such as a round and compact structure, good settling ability, high biomass retention and the ability to withstand high organic loading rates. Aerobic granulation technology has been demonstrated to be useful for a wide variety of wastewaters, including industrial, nutrient‐rich and toxic. This paper presents a state‐of‐the‐art review of effective aerobic granulation technology for wastewater treatment selected from the point‐of‐view of basic concepts of aerobic granulation, characterisation and factors that affect aerobic granulation, demonstrating the effectiveness of the cell‐immobilisation (aerobic granulation) technique. © 2012 Canadian Society for Chemical Engineering
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".