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Record W4387310511 · doi:10.2175/193864718825158990

Coca Cola Membrane Bioreactor Wastewater Treatment System

2023· article· en· W4387310511 on OpenAlexaboutno aff
Turner Grant, Mike Allison, Shannon Grant, Daniel Bertoldo, Bryan Connerat, Alecia Welsh, Jun Yum

Bibliographic record

VenueProceedings of the Water Environment Federation · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMembrane bioreactorWastewaterEffluentSewage treatmentWaste managementBioreactorAerationEnvironmental sciencePulp and paper industryEngineeringChemistry

Abstract

fetched live from OpenAlex

Coca Cola Membrane Bioreactor Wastewater Treatment SystemAbstractThe Coca-Cola Company has significantly upgraded the wastewater treatment at their Beverage Base Plant (BBP) in Atlanta, Georgia. Effluent quality has been improved with the upgrade to a biological treatment system known as a membrane bioreactor (MBR) consisting of an aeration tank and two membrane tanks. The membrane tanks make use of flat sheet membrane technology for solids/liquid separation. A screw press was installed for waste solids dewatering. The upgraded wastewater treatment plant ensured improved effluent quality well within the new limits of discharging to the local POTW. Keywords: membrane bioreactor, treatment plant upgrade, flat-plate membrane, beverage wastewater.The Coca-Cola Company has significantly upgraded the wastewater treatment at their Beverage Base Plant (BBP) in Atlanta, Georgia. Effluent quality has been improved with the upgrade to a biological treatment system known as a membrane bioreactor (MBR) consisting of an aeration tank and two membrane tanks. The upgraded wastewater treatment plant ensured improved effluent quality well within the new limits of discharging to the local POTW.SpeakerGrant, TurnerPresentation time13:30:0014:00:00Session time13:30:0015:00:00SessionFood &amp; Beverage: Upgrades and TroubleshootingSession locationRoom S403a - Level 4TopicFacility Operations and Maintenance, Intermediate Level, Sustainability and Climate Change, Water Reuse and ReclamationTopicFacility Operations and Maintenance, Intermediate Level, Sustainability and Climate Change, Water Reuse and ReclamationAuthor(s)Grant, TurnerAuthor(s)T. Grant <sup>1</sup>; M. Allison <sup>2 </sup>; S. Grant <sup>2</sup>; D. Bertoldo <sup>3</sup>; T. Grant <sup>1</sup>; B. Connerat <sup>4</sup>; A. Welsh <sup>4</sup>; J. Yum <sup>4</sup>;Author affiliation(s)Evoqua Water Technologies Canada Ltd., 370 Wilsey Road, Fredericton, NB E3B 6E9 <sup>1</sup>; Evoqua Water Technologies <sup>2 </sup>; Evoqua Water Technologies <sup>2</sup>; The Coca Cola Company <sup>3</sup>; Evoqua Water Technologies <sup>1</sup>; The Coca Cola Company <sup>4</sup>; The Coca Cola Company <sup>4</sup>; <sup>4</sup>;SourceProceedings of the Water Environment FederationDocument typeConference PaperPublisherWater Environment FederationPrint publication date Oct 2023DOI10.2175/193864718825158990Volume / Issue Content sourceWEFTECCopyright2023Word count8

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 score0.010
Threshold uncertainty score0.986

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)0.0000.001

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 head0.012
GPT teacher head0.179
Teacher spread0.167 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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