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Record W2161526922 · doi:10.1139/s05-038

Thermophilic anaerobic acid digestion of biosolids: hydrolysis, acidification, and optimization of retention time of acid digestion

2006· article· en· W2161526922 on OpenAlexvenueno aff
B. Puchajda, Jan A. Oleszkiewicz

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2006
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsnot available
Fundersnot available
KeywordsAcidogenesisAnaerobic digestionHydrolysisBiosolidsChemistrySewage sludgePopulationBiomass (ecology)Digestion (alchemy)Pulp and paper industryEnvironmental chemistryChromatographySewage treatmentEnvironmental engineeringBiochemistryAgronomyEnvironmental scienceBiologyMethaneOrganic chemistry

Abstract

fetched live from OpenAlex

The objective of the study was the optimization of the retention time in a thermophilic acid digestion system. To achieve this optimization, acidification index defined as the ratio between acidification rate and hydrolysis rate was used. First-order reaction model developed by Eastman and Ferguson (1981) was used to describe hydrolysis of particulate matter in sludge. The first-order hydrolysis rate constant was found to be 2.36 d –1 , and the initial concentration of degradable particulate COD was found to be 10.4 g/L. A model of acid digestion was proposed that included acidogenic biomass growth and decay. The total acidogenic population was estimated and kinetic parameters of acidogenic population were specific growth rate 0.78 d –1 , net specific growth rate 0.25 d –1 , decay coefficient 0.52 d –1 , and observed yield 0.05 mg biomass / mg COD consumed. Based on the acidification index, the optimum retention time in a thermophilic acid digester was 3–3.5 d. Key words: wastewater sludge, biosolids, anaerobic digestion, hydrolysis, acidification, kinetics.

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 score0.211
Threshold uncertainty score0.355

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 head0.161
Teacher spread0.157 · 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

Citations12
Published2006
Admission routes1
Has abstractyes

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