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Record W2053921657 · doi:10.2166/wst.2010.114

Treatment of synthetic kraft evaporator condensate using thermophilic and mesophilic membrane aerated biofilm reactors

2010· article· en· W2053921657 on OpenAlexafffund
Baoqiang Liao, M. R. Zheng, L. Ratana-Rueangsri

Bibliographic record

VenueWater Science & Technology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMesophileThermophileChemistryAerationKraft paperChemical oxygen demandPulp and paper industryWastewaterWaste managementOrganic chemistryBiologyBacteria

Abstract

fetched live from OpenAlex

A comparative study on the treatment of synthetic kraft evaporator condensate was conducted using thermophilic (55 degrees C) and mesophilic (30 degrees C) membrane aerated biofilm reactors (MABRs) and sequencing batch reactors (SBRs) for 8 months. Under tested conditions, a chemical oxygen demand (COD) removal efficiency of 80-95% was achieved with both thermophilic and mesophilic MABRs and SBRs. The COD removal efficiency of thermophilic MABR (80-90%) was slightly lower than that of the mesophilic MABR (85-95%) and the thermophilic SBR (90-95%). A significant amount (13-37%) of COD was stripped by conventional aeration in the SBRs, while stripping in MABRs was negligible. Simultaneous COD removal and denitrification were observed in the mesophilic MABR, while the thermophilic MABR contributed mainly for COD removal. Nitrification was not significant in both the thermophilic and mesophilic MABRs. The results suggest that treatment of kraft evaporator condensate is feasible with the use of both thermophilic and mesophilic MABRs in terms of COD removal with the advantages of negligible stripping.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.230
Teacher spread0.218 · 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 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

Citations7
Published2010
Admission routes2
Has abstractyes

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