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Record W4414693115 · doi:10.1016/j.psep.2025.107966

Comparative evaluation of suspended sludge performance at two reactor height-to-width ratios for high-ammonia anaerobically digested sludge supernatant treatment

2025· article· en· W4414693115 on OpenAlexaff
Yiduo Yao, Xin Zou, Yihui Zhang, Hengbo Guo, Jiyuan Xu, Yaman Boluk, Yang Liu

Bibliographic record

VenueProcess Safety and Environmental Protection · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDenitrifying bacteriaAnammoxNitrosomonasNitrogenPopulationBacteriaAmmoniaActivated sludge

Abstract

fetched live from OpenAlex

This study presents a comparative evaluation evaluation of two sequencing batch reactors (SBRs) with distinct height-to-width (H/W) ratios treating high-ammonia anaerobically digested sludge supernatant via the nitritation-denitritation process. The reactors were designed with constant volumes but differing H/W ratios, 4.8 for R1 and 2.8 for R2, while maintaining identical superficial gas velocity (SGV), to isolate the effects of reactor configuration. Both systems consistently achieved ~99% ammonium nitrogen (NH 4 + -N) removal throughout operation, with total inorganic nitrogen removal efficiencies of 95% in R1 and 97% in R2. Kinetic analysis revealed that R2 achieved higher microbial activity, with maximum AOB and denitritation activities of 0.57 and 6.32 g N/g VSS/d, respectively, compared to 0.49 and 5.12 g N/g VSS/d in R1. Conversely, R1 demonstrated better sludge settleability, with final SVI 30 values of 90 mL/g compared to 120 mL/g in R2. Microbial community profiling revealed comparable population structures in both reactors, with both systems dominated by the same key functional microorganisms: Nitrosomonas as the predominant ammonia-oxidizing bacteria (AOB) and Thauera as the primary denitrifying bacteria. These findings indicate that while a lower H/W ratio enhances microbial activity and nitrogen removal, a higher H/W ratio improves sludge compaction and settling.

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 categoriesnone
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 score0.069
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.025
GPT teacher head0.264
Teacher spread0.239 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2025
Admission routes1
Has abstractyes

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