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Start-Up of an Anaerobic Moving Bed–Biofilm Reactor and Transition to Brewery Wastewater Treatment

2016· article· en· W2464224916 on OpenAlexaff
Alessandro di Biase, Tanner R. Devlin, Jan A. Oleszkiewicz

Bibliographic record

VenueJournal of Environmental Engineering · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Fuel Cells and Bioremediation
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsChemical oxygen demandWastewaterBiogasMethanePulp and paper industryChemistryBiomass (ecology)Volume (thermodynamics)Anaerobic digestionAnaerobic exerciseEnvironmental scienceSewage treatmentEnvironmental engineeringAnimal scienceWaste managementEcologyBiology

Abstract

fetched live from OpenAlex

Two 4-L anaerobic moving bed–biofilm reactors (AMBBR) with different quantities of cubic media (25 and 35% by volume) were examined. Reactors were fed synthetic protein-based feed and both were shown to be capable of treating up to 20 kg/m3 day of chemical oxygen demand (COD), achieving 80% COD removal. Beyond that load the performance began to decrease. The maximum biogas production observed was 9.25 m3 CH4/m3 day at an organic loading rate (OLR) of 20 kg COD/m3 day. The methane composition varied between 60 and 70%. Averaged methane yields of 0.36±0.07 m3 CH4/kg COD removed and specific removal rates from 0.4 to 20 kg COD/m3 day were achieved. At the maximum OLR above 20 kg COD/m3 day, the specific surface area activity was calculated as 98 and 75 g COD/m2 day in the 25 and 35% reactors, respectively. Kinetic tests performed at the maximum OLR revealed that free-floating suspended biomass contributed to no more than 2.5% of the total removal. The reactors were also capable of treating brewery wastewater, reliably attaining 80% COD removal with methane yields of 0.36±0.06 and 0.39±0.03 m3 CH4/kg COD from the 35 and 25% reactors, 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 score0.038
Threshold uncertainty score0.503

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 head0.005
GPT teacher head0.167
Teacher spread0.163 · 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

Citations17
Published2016
Admission routes1
Has abstractyes

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