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Enhanced biofilm formation and municipal wastewater treatment efficiency using granular activated carbon modified bio-ball carriers in moving bed biofilm reactor

2025· article· en· W4412076553 on OpenAlexafffund
Xinya Yang, Yun Zhou, Lei Zhang, Chelsea Benally, Yang Liu

Bibliographic record

VenueBioresource Technology · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsUniversity of Alberta
FundersChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsBiofilmWastewaterMoving bed biofilm reactorActivated carbonSewage treatmentChemistryWaste managementChemical engineeringPulp and paper industryEnvironmental engineeringEnvironmental scienceBacteriaAdsorptionEngineeringBiology

Abstract

fetched live from OpenAlex

This study introduces a novel enhancement to biological wastewater treatment by integrating Granular Activated Carbon (GAC) with plastic bio-balls in a Moving Bed Biofilm Reactor (MBBR) configuration treating municipal sewage. The resulting GAC-MBBR system demonstrated significantly improved treatment efficiency, achieving 81.8 % carbon and 74.9 % nitrogen removal under high loading conditions-outperforming the conventional plastic-MBBR (64.5 % and 62.7 %, respectively). The macroporous structure of GAC provided increased surface area, promoting superior biofilm growth and microbial retention. Enrichment of key functional genera, including Zoogloea, Thauera, Nitrospira, and Nitrosomonas, was observed, indicating enhanced nitrification potential. Additionally, greater biomass accumulation on GAC carriers underscored their effectiveness in supporting microbial aggregation. These findings suggest that incorporating GAC into MBBR systems offers a promising strategy to optimize biofilm development and improve nutrient removal in wastewater treatment.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.229
Teacher spread0.217 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations9
Published2025
Admission routes2
Has abstractyes

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