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Record W3213861742

Life-cycle Assessment of Full-scale Membrane Bioreactor and Tertiary Treatment Technologies in Fruit Processing Industry

2019· dissertation· en· W3213861742 on OpenAlexaff
Tong Chu

Bibliographic record

VenueThe Atrium (University of Guelph) · 2019
Typedissertation
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBioreactorScale (ratio)Membrane bioreactorSCALE-UPProcess engineeringLife-cycle assessmentEnvironmental scienceEngineeringManufacturing engineeringProduction (economics)BiologyEconomicsGeographyBotany
DOInot available

Abstract

fetched live from OpenAlex

Life-cycle assessment (LCA) was conducted to quantitatively assess the total environmental impacts of membrane bioreactor (MBR) and tertiary technologies treating wastewater in the fruit processing sector, allowing comparisons on the impacts of different treatment options, including impacts without on-site treatment. The system boundaries for all scenarios comprise raw materials extraction and processing, transportation, construction, operation and waste disposal. SimaPro 8.0.4.26 was used as the software tool, and two impact assessment methods (ReCiPe v1.11 and TRACI v2.1) were applied. Results showed that MBR combined with RO and UV contributed the least damage to the ecosystem, and minimized eutrophication impacts from the sewage when compared to the non-treatment scenario. Treating wastewater in municipal wastewater treatment plants (WWTP) would mitigate eutrophication effects, but it resulted in more environmental impacts from categories such as climate change and human health compared with implementing on-site treatment systems.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score0.592

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 head0.010
GPT teacher head0.223
Teacher spread0.213 · 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 designObservational
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
Published2019
Admission routes1
Has abstractyes

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