Anaerobic moving-bed biofilm reactors for the treatment of wastewater: a review of applicability
Bibliographic record
Abstract
The use of anaerobic digestion for wastewater treatment continues to be increasingly valued due to the need for resource preservation and recovery. Different high-rate anaerobic reactors with biomass retention capacity exist for the treatment of industrial and municipal wastewaters. The anaerobic moving-bed biofilm reactor (AnMBBR) is a newer anaerobic reactor that operates with biofilm growing on mobile inert media. It is simpler in design and operation compared to other high-rate reactors and it can withstand high concentrations of suspended solids. The number of studies on AnMBBRs for wastewater treatment has been increasing; however, until now no systematic evaluation of the scientific literature on this topic exists. This review aims to identify the types of wastewaters treatable using AnMBBRs, the process configurations for best treatment performance, and advantages/disadvantages of AnMBBRs.AnMBBR is suitable for wastewater treatment at high organic loads, as it allows for high volumetric loading rates and short retention times, resulting in a compact system. It can tolerate large variations of organic and hydraulic loads and even starvation periods. This flexibility makes AnMBBR a suitable option for the treatment of industrial wastewaters experiencing seasonal variability in production levels or changes in product lines. Overall, AnMBBR technology is a versatile and effective option for the treatment of various wastewaters, offering high removal efficiencies, stability, and flexibility in operation, even at temperatures lower than the typical mesophilic range used in anaerobic treatment. Its potential for application is expected to continue growing along the need for resource recovery from wastewaters.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| 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) | 0.004 | 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".