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Record W3117844453 · doi:10.2166/wst.2020.598

Biomethane production improvement by hydrothermal pretreatment of thickened waste activated sludge

2020· article· en· W3117844453 on OpenAlexafffund
Ahmad Shabir Razavi, Farokhlaqa Kakar, E. Hosseini Koupaie, Hisham Hafez, Elsayed Elbeshbishy

Bibliographic record

VenueWater Science & Technology · 2020
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsGreenfield Research (Canada)Toronto Metropolitan University
FundersOntario Water Consortium
KeywordsMesophileBiogasAnaerobic digestionChemistryPulp and paper industryHydrothermal circulationActivated sludgeSolubilizationBiodegradationBiogas productionNuclear chemistryWaste managementMethaneWastewaterEnvironmental scienceChemical engineeringEnvironmental engineeringBiochemistryOrganic chemistryBiology

Abstract

fetched live from OpenAlex

This study evaluated the impact of hydrothermal pretreatment on thickened waste activated sludge (TWAS) for solubilization enhancement and biomethane production improvement through the mesophilic anaerobic digestion process. In order to assess the effect of temperature, retention time and severity index (SI) of the hydrothermal pretreatment, TWAS was exposed to fifteen different pretreatment conditions within a combination of 10 different pretreatment temperature range (150-240 °C), five different retention times (5-30 min) and five different severity indexes (SI = 3, 3.5, 4, 4.5 and 5). The solubilization enhancement was observed in all hydrothermally pretreated samples with the highest solubilization efficiency of 49% in pretreatment conditions of 200 °C and 10 min retention time within the corresponding SI = 4. Biomethane production was not improved in all fifteen pretreatment conditions, pretreatment with SI beyond 4 decreased the biodegradability of TWAS. The highest biomethane production was observed in the pretreatment condition of 170 °C and 10 min with a 40% increase compared to non-pretreated TWAS.

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 score0.010
Threshold uncertainty score0.346

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.200
Teacher spread0.192 · 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

Citations10
Published2020
Admission routes2
Has abstractyes

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