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Record W2066142011 · doi:10.1080/09593332608618514

Cell Agglomeration in Acidogenic, Mixed, and Methanogenic Cultures at Different Loading and Mixing Conditions

2005· article· en· W2066142011 on OpenAlexaff
M. Verma, T.R. Sreekrishnan, R. D. Tyagi

Bibliographic record

VenueEnvironmental Technology · 2005
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsInstitut National de la Recherche Scientifique
FundersIndian Institute of Technology Delhi
KeywordsAcidogenesisMixing (physics)Economies of agglomerationChemical engineeringChemistryWaste managementPulp and paper industryEnvironmental scienceMaterials scienceEnvironmental engineeringAnaerobic digestionMethanePhysicsEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Cell agglomeration studies were conducted in anaerobic fluidized bed reactors without any external support. Granulation was studied at different combinations of loading and mixing conditions utilizing synthetic wastewater. Both single-stage and two-stage biomethanation processes were studied. Reactors of volume 0.55, 10, and 16 l were operated with height to diameter ratio of 7-8. It was found that the acidogens were more liable to form granules among mixed culture of anaerobes, while the methanogens were capable of forming cell agglomerates in the form of flocs. In acidogenic granules, rod shaped bacteria were dominating, while in case of methanogens, there were more cocci. It was found that for an upflow liquid linear velocity upto 2.0 m h(-1), acidogens showed very good granulation but at relatively higher values of upflow liquid linear velocities granulation was affected adversely, causing breakage and dissociation of granules. In the case of methanogens and mixed process, it was found that upflow liquid linear velocities up to 4.0 m h(-1) were suitable and biomass flocs were actively growing. The maximum organic loadings applied were 39.0 and 54.4 kg COD m(-3) d(-1) (at 27.3 and 35.36 kg COD m(-3) d(-1) degradation respectively) for single-stage and two-stage biomethanation respectively.

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 score0.007
Threshold uncertainty score0.412

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 head0.004
GPT teacher head0.185
Teacher spread0.181 · 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

Citations4
Published2005
Admission routes1
Has abstractyes

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