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Record W60197488 · doi:10.2175/106143005x51941

Effect of a Starch-Rich Industrial Wastewater on the Acid-Phase Anaerobic Digestion Process

2005· article· en· W60197488 on OpenAlexafffund
P. Elefsiniotis, David G. Wareham, M. Smith

Bibliographic record

VenueWater Environment Research · 2005
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAcidogenesisChemistryIndustrial wastewater treatmentAnaerobic digestionChemical oxygen demandWastewaterStarchPulp and paper industryAcetic acidBioreactorVolatile suspended solidsFood scienceMethaneEnvironmental scienceEnvironmental engineeringBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

This research investigated the effect of varying the starch-rich, industrial-wastewater component of mixtures with municipal wastewater fed to an anaerobic digester. A laboratory-scale, completely-mixed anaerobic digester was operated at an HRT of 30 h, an SRT of 10 d, and an ambient temperature of 21.5 +/- 1.5 degrees C. The industrial-to-municipal ratios tested were 1:3, 1:1, 3:1, and 100% industrial by volume. Steady-state, acidogenic conditions were achieved for all runs, except 100% industrial. The pH was observed to drop substantially as the industrial constituent of the feed increased. Net volatile fatty acids (VFA) production reached a plateau of approximately 800 mg/L at ratios of 1:1 and higher, while volatile suspended solids (VSS) reduction steadily increased as the industrial component rose. The specific VFA and soluble chemical oxygen demand (SCOD) production rates leveled off at approximately 0.070 mgVFA/ mgVSS.d and 0.124 mgSCOD/mgVSS.d, respectively, for all the mixtures investigated, except for 100% industrial. In this latter case, both rates dropped dramatically. Finally, acetic and propionic acid concentrations fell as the industrial proportion of the mixture increased. This was compensated by a rise in butyric acid production.

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)0.002
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 score0.080
Threshold uncertainty score0.813

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0010.001

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 head0.036
GPT teacher head0.299
Teacher spread0.263 · 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

Citations27
Published2005
Admission routes2
Has abstractyes

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