A review on the advances in nitrifying biofilm reactors and their removal rates in wastewater treatment
Bibliographic record
Abstract
Abstract Growing demand for efficient wastewater treatment systems is spurring on the development of new technologies. Biofilm‐based reactors can be used for the treatment of a variety of wastewaters and these reactors are resistant to toxic environments. Bioreactors, such as sequencing batch biofilm and moving bed biofilm are advanced techniques to treat various types of wastewaters with diverse operating conditions. Ammonium oxidizing bacteria, nitrite oxidizing bacteria and Anammox (anaerobic ammonium oxidation) bacteria are reported to be responsible for nutrient removal. In recent decades, the performance of these systems has been studied widely and compared for a number of wastewater treatment applications. In general, they are particularly suitable for high‐rate nitrification and nitrogen removal. The efficiency of these reactors has been confirmed in the laboratory and large‐scale plants. Their efficiency depends on the surface area of the biocarrier, the filling percentage volume of biofilm carriers, organic loading and diffused aeration supply. Chemical oxygen demand removal of 50–98% was reported for <12 h hydraulic retention time, 0.2 to 6.5 mg L −1 dissolved oxygen concentration and temperature range 15–35 °C. Also, the ratio of nitrate to ammonium conversion was from 0.2 to 90 and N 2 conversion was from 0 to 8.5 mg. This review studied each of these bioreactors in the removal of nutrients (nitrogen, phosphorus and oxygen) from different wastewaters and compared them to conventional treatment. The review also includes the relevant studies on laboratory and pilot‐scale bioreactors to enhance their performance and reduce their costs. © 2018 Society of Chemical Industry
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".