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Record W2888131277 · doi:10.1680/jenes.18.00017

Energy recovery from municipal wastewater: impacts of temperature and collection systems

2018· article· en· W2888131277 on OpenAlexaffvenueabout
Mengjiao Gao, Lei Zhang, Huixin Zhang, Ariovaldo O. Florentino, Yang Liu

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2018
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBlackwaterToiletAnaerobic digestionWastewaterSewageEnvironmental scienceGreywaterSewage treatmentWaste managementMethaneEnvironmental engineeringEcologyBiologyEngineering

Abstract

fetched live from OpenAlex

Municipal wastewater contains valuable organic contents that can be recovered as energy through anaerobic digestion. The operating temperature impacts the design and performance of anaerobic digestion systems and can be selected to accommodate different geographical zones, from tropical to frigid. Here the biological methane potential (BMP) and the effect of temperature on methane production are compared for municipal wastewater collected from different collection systems, including source-diverted toilet wastewater (i.e. blackwater) and conventionally collected sewage. The sewage and blackwater collections were performed in Canada. BMP tests were conducted at 20 and 35°C. The BMPs of municipal sewage and water-wasting toilet (6–9 l per toilet flush) blackwater were comparable at 20 and 35°C. Water-conserving toilet (1 l per toilet flush) blackwater had lower BMP than municipal sewage and water-wasting toilet blackwater at 35°C, due to FA inhibition. Under the lower-temperature condition (20°C), although no apparent inhibition in the methanogenic process was observed for water-conserving toilet blackwater, a much lower hydrolysis rate was observed.

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 score0.205
Threshold uncertainty score0.216

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 head0.004
GPT teacher head0.163
Teacher spread0.159 · 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
Published2018
Admission routes3
Has abstractyes

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