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Potential Water Reuse for High Strength Fruit and Vegetable Processor Wastewater with an MBR

2016· article· en· W2541497114 on OpenAlexafffund
Adam Moore, Richard G. Zytner, Sheng Chang

Bibliographic record

VenueWater Environment Research · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Guelph
FundersOntario Ministry of Agriculture, Food and Rural Affairs
KeywordsKjeldahl methodWastewaterEffluentMembrane bioreactorUltrafiltration (renal)ReuseBiochemical oxygen demandEnvironmental sciencePulp and paper industryWastewater reuseSewage treatmentNitrificationChemical oxygen demandGreywaterEnvironmental engineeringWaste managementChemistryNitrogenEngineeringChromatography

Abstract

fetched live from OpenAlex

High strength food processing wastewater from two processing plants was studied to determine the effectiveness of an aerobic membrane bioreactor (MBR) to reduce BOD, TSS and nutrients below municipal sewer discharge limits. The MBR comprised a 20 L lab-scale reactor combined with a flat sheet, ultrafiltration membrane module. The parameters studied included the operational flux, solids and hydraulic retention times and recirculation ratio with regards to nitrification/denitrification. The MBR system provided excellent removal efficiency at 97% COD, 99% BOD, 99.9% TSS, 90% TKN, and 60% TP for both processing plants, which eliminated the surcharges, allowing the firms to stay competitive. Effluent reuse tests showed that activated carbon proved effective in removing color from the MBR permeate, while UV treatment was able to achieve a 5 log reduction in bacteriophage. Overall, these treatment successes show the potential for water reuse in the agrifood sector.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.018
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.024
GPT teacher head0.267
Teacher spread0.243 · 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; both teacher heads agree on what is shown here.

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
Published2016
Admission routes2
Has abstractyes

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