Microalgae-bacteria symbiosis enhanced nitrogen removal from wastewater in an inversed fluidized bed bioreactor: performance and microflora
Bibliographic record
Abstract
Conventional wastewater biological nitrogen removal (BNR) processes require a large amount of air and external organic carbon, causing a significant increase in operating costs and potential secondary pollution. Herein, this study investigated the nitrogen removal performance and the underlying mechanisms of a novel simultaneous nitrification and denitrification (SND) coupled with photoautotrophic assimilation system in an inversed fluidized bed bioreactor (IFBBR). Nitrogen removal was achieved through the synergistic interaction of microalgae and bacteria, with microalgae providing O2 for nitrification and microbial biomass decay supplying organic carbon for denitrification. The IFBBR was continuously operated for more than 240 days without aeration and external organic carbon, the total nitrogen (TN) removal efficiency reached over 95%. A novel C-N-O dynamic balance model was constructed, revealing that nitrification and denitrification were the primary pathways for nitrogen removal. The model further quantified the microbial contributions, showing that microalgae generated O2 at a rate of 81.82 mg/L·d, while microbial biomass decay released organic carbon at a rate of 148.66 mg/L·d. Microbial diversity analysis confirmed the majority presence of microalgae (Trebouxiophyceae), nitrifying bacteria (Gordonia and Nitrosomonas) and denitrifying bacteria (Ignavibacterium and Limnobacter). This study successfully achieved enhanced nitrogen removal without the need for aeration or external organic carbon. These advancements provide valuable insights into efficient wastewater nitrogen removal, offering significant benefits in terms of reduced energy consumption, lower operational costs, and decreased CO2 emissions.
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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) | 0.001 | 0.001 |
| 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 | 0.000 | 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".