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Record W2073280416 · doi:10.2166/wst.2010.939

Treatment of thermomechanical pulping condensate using thermophilic and mesophilic sequencing batch reactors

2010· article· en· W2073280416 on OpenAlexafffund
Shawn P. Langevin, Baoqiang Liao

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
KeywordsMesophilePulp and paper industryEffluentThermophileChemical oxygen demandChemistryAerationWaste managementWastewaterPaper millSequencing batch reactorSewage treatmentBiologyBacteriaOrganic chemistry

Abstract

fetched live from OpenAlex

In-mill thermophilic treatment of individual wastewater streams to achieve water system closure has received much attention in pulp and paper mills. Aerobic biological treatment of thermomechanical pulping (TMP) condensate was conducted using thermophilic (55 °C) and mesophilic (35 °C) sequencing batch reactors (SBRs) for a period of 143 days at a cyclic time of 6, 8 and 12 h. A soluble chemical oxygen demand (SCOD) removal efficiency of 77 to 91% was achieved, given an organic loading rate of 0.7-1.3 kg/m³ d. The COD removal efficiency of the thermophilic SBR was slightly lower than that of the mesophilic SBR. Majority of the soluble COD was removed by biodegradation with a small portion (9-13%) of soluble COD stripped by aeration. The settleability (sludge volume index) and the flocculating ability (effluent suspended solids) of thermophilic sludge were comparable to or slightly poorer than that of the mesophilic sludge. The level of filaments in thermophilic sludge was usually higher than that in mesophilic sludge. The results of the study indicate that both thermophilic and mesophilic SBRs can be successfully operated for in-mill treatment of TMP condensate. The treated effluent has the potential for subsequent reuse in the mill.

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 score0.013
Threshold uncertainty score0.742

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
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 head0.019
GPT teacher head0.241
Teacher spread0.222 · 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

Citations6
Published2010
Admission routes2
Has abstractyes

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