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Record W4403406945 · doi:10.1021/acsestengg.4c00345

Optimization of Dry Anaerobic Digestion of Food Waste in Leachate Bed Reactors

2024· article· en· W4403406945 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueACS ES&T Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsCarleton UniversityUniversity of Waterloo
FundersNational Research Foundation of KoreaMinistry of Education
KeywordsLeachateAnaerobic digestionFood wasteWaste managementEnvironmental scienceDigestion (alchemy)Pulp and paper industryChemistryMethaneEngineeringChromatography

Abstract

fetched live from OpenAlex

This study investigates the optimization and performance of a single-stage leachate bed reactor (LBR) system for the dry anaerobic digestion (AD) of food waste (FW). Three different parameters were assessed in the LBR run at a reaction time of 10 days: the inoculum-to-substrate ratio (ISR), leachate recirculation rate, and type of inoculum. For ISR optimization, four different ISRs were investigated ranging between 10 and 60%. Results indicated that a higher ISR of 60% with an acclimated inoculum led to a 3.35-fold increase in cumulative methane yield compared to an ISR of 10%, while volatile solids (VS) reduction with an ISR of 10% was better than that with an ISR of 60%. Furthermore, increasing leachate recirculation rates improved methane yield, with a notable 78% increase observed when the recirculation rate was elevated from 0.3 to 7.5 L/h. These results demonstrate high methane production of 349 mLCH 4 /gVS reduced within a short digestion time of 10 days.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.290
Threshold uncertainty score0.589

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 head0.007
GPT teacher head0.187
Teacher spread0.179 · 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