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Record W4411227199 · doi:10.3934/mine.2025015

Modeling nitrogen removal in membrane aerated biofilm reactors: the role of nitritation, denitritation, and anammox nitrogen removal

2025· article· en· W4411227199 on OpenAlexaff
Maryam Ghasemi, Sheng Chang

Bibliographic record

VenueMathematics in Engineering · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsUniversity of GuelphUniversity of Waterloo
Fundersnot available
KeywordsAnammoxAerationNitrogenBiofilmChemistryPulp and paper industryDenitrificationBiologyBacteriaEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

This study develops a two-dimensional, multi-species biofilm model to investigate the influence of environmental factors, specifically temperature and concentrations of oxygen, acetate, and ammonium on nitrogen removal in membrane aerated biofilm reactors (MABRs). The resulting model is a highly nonlinear reaction-diffusion system, explored through computer simulations, and captures microbial interactions, substrate transport, and nitrogen transformations within a biofilm, incorporating the counter-diffusion mechanism. Three nitrogen removal pathways have been examined in this study: nitritation-denitritation (ND), partial nitrification-anammox (PN/A), and conventional nitrification-denitrification (CND). The simulation results show that temperature and concentrations of oxygen and acetate significantly affect nitrogen removal rates and contributions of each pathway. ND dominates under most conditions, while PN/A prevails in oxygen-limited scenarios ($ O_{\infty} = 0.25-0.5\; gm^{-3} $) and co-dominates with ND at moderate oxygen levels ($ O_{\infty} = 0.5-1\; gm^{-3} $). CND is significant only at higher oxygen concentrations ($ O_{\infty} = 5\; gm^{-3} $) with low ammonium ($ N_{1\infty} = 5-15\; gm^{-3} $) and acetate levels ($ A_{\infty} = 6\; gm^{-3} $). Moreover, it has been shown that temperature enhances nitrogen removal primarily by increasing the contribution of anammox. Effective removal rates ($ > 0.1\; g/m^2/d $) occur at $ O_{\infty}\geq 1\; gm^{-3} $ with low to moderate acetate levels ($ A_{\infty} = 6\; gm^{-3} $ to $ < 100\; gm^{-3} $). The simulations further indicate that MABRs can achieve a stable ND nitrogen removal efficiency with biofilm thickness exceeding approximately $ 0.8\; mm $. In this scenario, ammonium-oxidizing bacteria (AOB) and ND denitrifiers outcompete aerobic heterotrophs and nitrite-oxidizing bacteria, resulting in a biofilm structure predominantly composed of AOB and ND denitrifiers. The findings of this study provide valuable insights for optimizing MABR design and operation to achieve energy efficient nitrogen removal.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.206
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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