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Record W2970549273 · doi:10.3390/pr7090576

Effect of Hydrothermal Pretreatment on Volatile Fatty Acids Production from Source-Separated Organics

2019· article· en· W2970549273 on OpenAlexafffund
Farokh Laqa Kakar, E. Hosseini Koupaie, Hisham Hafez, Elsayed Elbeshbishy

Bibliographic record

VenueProcesses · 2019
Typearticle
Languageen
FieldEngineering
TopicProcess Optimization and Integration
Canadian institutionsGreenfield Research (Canada)Toronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of CanadaOntario Water Consortium
KeywordsChemistryHydrothermal circulationMineralization (soil science)Yield (engineering)Raw materialChemical oxygen demandSolubilizationFood scienceEnvironmental chemistryNitrogenBiochemistryOrganic chemistryWaste managementChemical engineering

Abstract

fetched live from OpenAlex

The current study investigates the effect of hydrothermal pretreatment (HTP) on acidification of source-separated organics (SSO) in terms of volatile fatty acids (VFAs) production and solubilization. Temperature and retention time for HTP of SSO ranged from 150 to 240 °C and 5 to 30 min, respectively. The soluble substance after hydrothermal pretreatment initially increased, reaching its peak at 210 °C and then declined gradually. The highest overall chemical oxygen demand (COD) solubilization of 63% was observed at “210 °C-20 min” compared to 17% for raw SSO. The highest VFAs yield of 1536 mg VFAs/g VSS added was observed at “210 °C-20 min” compared to 768 mg VFAs/g VSS for raw SSO. Intensification of hydrothermal pretreatment temperature beyond 210 °C resulted in the mineralization of the organics and adversely affected the process.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.205
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2019
Admission routes2
Has abstractyes

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