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Record W1994590920 · doi:10.2202/1934-2659.1429

Potential Impact on Activated Sludge Treatment from the Implementation of Cellulosic Ethanol Production at a Pulp and Paper Mill

2009· article· en· W1994590920 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChemical Product and Process Modeling · 2009
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsNewsprintCellulosic ethanolAerationPulp and paper industryEnvironmental scienceDewateringBiosolidsEthanol fuelWaste managementActivated sludgePaper millSewage treatmentBiofuelEffluentChemistryEnvironmental engineeringCelluloseEngineeringKraft paper

Abstract

fetched live from OpenAlex

The potential impacts of additional flow and organic loads resulting from the production of cellulosic ethanol on an existing integrated newsprint mill were simulated in this paper. It was found that depending on the ethanol production rate and the existing spare capacity for additional biochemical oxygen demand (BOD), treatment plant modifications may be required. In terms of operating costs, it was found that nutrients use could increase by 50% to 150%, while aeration flow could increase by 5% to 140% depending on the desired level of dissolved oxygen in the aeration basin. Significant increases in polymer use for mixed sludge dewatering could result due to additional biosolids production. Additional capital costs for air blowers could also be necessary unless the mill has existing spare capacity. It was also found that ethanol recovery efficiency and production rate had little impact on BOD removal up to a certain ethanol production level, and that the impact on operating costs decreased with increasing recovery.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Simulation or modelinghigh
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Simulation or modelinghigh
models agreeAgreement compares identical category sets and study designs across arms.

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 score0.022
Threshold uncertainty score0.470

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 head0.014
GPT teacher head0.254
Teacher spread0.241 · 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