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Characterization of Hydrolysis Kinetics in Staged Anaerobic Digestion of Wastewater Treatment Sludge

2017· article· en· W2789774261 on OpenAlexaff
Mirzaman Zamanzadeh, Wayne J. Parker

Bibliographic record

VenueWater Environment Research · 2017
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMesophileAnaerobic digestionHydrolysisChemistryHydraulic retention timeChemical oxygen demandSewage sludgeWastewaterPulp and paper industryThermal hydrolysisSewage treatmentWaste managementSewage sludge treatmentEnvironmental engineeringEnvironmental scienceMethaneBiochemistryOrganic chemistryBiology

Abstract

fetched live from OpenAlex

The hydrolysis of mixed primary and secondary sludges in two-stage anaerobic digestion was evaluated and compared with conventional single-stage digestion, using various temperature-phased configurations of M1-M2, M1-T3, T1-T2, and T1-M3. A dual hydrolysis model best described the hydrolysis in all tests. This model was also able to consistently estimate the readily and slowly fractions of particulate chemical oxygen demand (COD) of raw sludge used in the tests. The hydrolysis kinetic coefficients (Khyd_s and Khyd_r) estimated for the mesophilic digesters were significantly greater in the short hydraulic retention time (HRT) M1 digester than those of the extended HRT digesters. Conversely, at thermophilic temperatures only Khyd_r was greater in short HRT T1 digester when compared to the extended HRT digesters. The increased Khyd_r and reduced Khyd_s values due to staging effect were explained with surface reaction models and endogenous decay. The temperature dependency of Khyd_s and Khyd_r was also explored in the staged digesters.

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 score0.114
Threshold uncertainty score0.307

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 head0.033
GPT teacher head0.262
Teacher spread0.228 · 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
Published2017
Admission routes1
Has abstractyes

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