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Record W2014976644 · doi:10.1021/ef901255k

Modeling the Effect of Sonication on the Anaerobic Digestion of Biosolids

2010· article· en· W2014976644 on OpenAlexfundno aff
Saad Aldin, Elsayed Elbeshbishy, George Nakhla, Madhumita B. Ray

Bibliographic record

VenueEnergy & Fuels · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSonicationChemical oxygen demandAnaerobic digestionChemistryActivated sludgeBiosolidsPulp and paper industryLysisAnaerobic exerciseWastewaterSewage treatmentChromatographyMethaneEnvironmental scienceBiochemistryEnvironmental engineeringBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

Ultrasound treatment of wastewater sludge prior to anaerobic digestion disrupts the flocs and causes lysis of the bacterial cells, releasing both inter- and intracellular materials. Primary and waste-activated sludge (WAS) were treated with different ultrasonic intensities, varying sonication time and amplitude at a constant frequency. Results showed that the gas production, volatile fatty acids, ratio of soluble chemical oxygen demand to total chemical oxygen demand, and soluble protein increased, while the particulate protein and particle size of the sludge decreased, with sonication time. An empirical model was developed to determine the economic viability of ultrasound based on electrical energy input and energy obtained from enhanced methane production. It has been found that ultrasonic pretreatment is only economically viable for primary sludge at low sonication doses. The Anaerobic Digestion Model No. 1 (ADM1) was applied to the batch anaerobic digestion for sonicated and non-sonicated sludge. In almost all cases, the model successfully simulated the experimental trends.

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 score0.031
Threshold uncertainty score0.191

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 head0.009
GPT teacher head0.230
Teacher spread0.220 · 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

Citations23
Published2010
Admission routes1
Has abstractyes

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