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Record W3163785279 · doi:10.3390/en14102948

Decentral Energy Generation Potential of Anaerobic Digestion of Black Water and Kitchen Refuse for Eco-District Planning

2021· article· en· W3163785279 on OpenAlexafffund
Soufia Mohammadi, Pilar Monsalvete, Ursula Eicker

Bibliographic record

VenueEnergies · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsConcordia University
FundersCanada Excellence Research Chairs, Government of Canada
KeywordsAnaerobic digestionBiogasBlackwaterBioenergyWaste managementEnvironmental scienceBiomass (ecology)MethaneDigestion (alchemy)Environmental engineeringFood wasteBiofuelEngineeringChemistryEcology

Abstract

fetched live from OpenAlex

Biogas technology is an important alternative energy source worldwide. Blackwater and kitchen refuse represent ideal waste streams for bioenergy recovery through anaerobic co-digestion. Modeling of the biokinetics of anaerobic digestion on several aspects, such as microbial activity, substrate degradation, and methane production, from co-digestion of black water (BW) and kitchen refuse (KR) was the objective of this research. A mathematical model was developed towards a simulation based on mass balances on biomass, the organic substrate, and biogas. The model was implemented in INSEL and experimental data from the literature were used for model validation. The study shows that the simulation results fit well with the experimental data. The energy consumption and generation potential of anaerobic co-digestion of BW and KR were calculated to investigate if the produced biogas could supply the digester’s energy demand. This study can be used to pre-design anaerobic digestion systems in eco-districts.

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 score0.115
Threshold uncertainty score0.411

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 head0.014
GPT teacher head0.215
Teacher spread0.201 · 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

Citations9
Published2021
Admission routes2
Has abstractyes

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