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Record W2112625730 · doi:10.3390/en6126211

Rapid Biogas Production by Compact Multi-Layer Membrane Bioreactor: Efficiency of Synthetic Polymeric Membranes

2013· article· en· W2112625730 on OpenAlexaff
Supansa Youngsukkasem, Hamidreza Barghi, Sudip Kumar Rakshit, Mohammad J. Taherzadeh

Bibliographic record

VenueEnergies · 2013
Typearticle
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsLakehead University
FundersVetenskapsrådetEuropean Commission
KeywordsMembraneBioreactorBiogasChemical engineeringPolyvinylidene fluorideMembrane bioreactorPolyamideChemistryChromatographyMembrane reactorHydraulic retention timeMaterials scienceWaste managementWastewaterBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Entrapment of methane-producing microorganisms between semi-permeable synthetic membranes in a multi-layer membrane bioreactor (MMBR) was studied and compared to the digestion capacity of a free-cell digester, using a hydraulic retention time of one day and organic loading rates (OLR) of 3.08, 6.16, and 8.16 g COD/L·day. The reactor was designed to retain bacterial cells with uprising plug flow through a narrow tunnel between membrane layers, in order to acquire maximal mass transfer in a compact bioreactor. Membranes of hydrophobic polyamide 46 (PA) and hydroxyethylated polyamide 46 (HPA) as well as a commercial membrane of polyvinylidene fluoride (PVDF) were examined. While the bacteria in the free-cell digester were washed out, the membrane bioreactor succeeded in retaining them. Cross-flow of the liquid through the membrane surface and diffusion of the substrate through the membranes, using no extra driving force, allowed the bacteria to receive nutrients and to produce biogas. However, the choice of membrane type was crucial. Synthesized hydrophobic PA membrane was not effective for this purpose, producing 50–121 mL biogas/day, while developed HPA membrane and the reference PVDF were able to transfer the nutrients and metabolites while retaining the cells, producing 1102–1633 and 1016–1960 mL biogas/day, respectively.

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 score0.030
Threshold uncertainty score0.832

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)0.0010.000

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 head0.015
GPT teacher head0.218
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 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

Citations24
Published2013
Admission routes1
Has abstractyes

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