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Record W2084455060 · doi:10.5004/dwt.2009.648

Market and design considerations of the 37 larger MBR plants in Europe

2009· article· en· W2084455060 on OpenAlexaff
B. Lesjean, Vincent Ferré, Enrico Vonghia, H. Moeslang

Bibliographic record

VenueDesalination and Water Treatment · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsSheridan College
FundersEuropean Commission
KeywordsFiltration (mathematics)Maturity (psychological)Environmental engineeringEnergy demandEnvironmental scienceEngineeringEnvironmental economicsEconomicsMathematics

Abstract

fetched live from OpenAlex

By the end of 2007, 10 years after the commissioning of the first full-scale municipal MBR plant in Europe, 37 large MBR plants with a nominal capacity greater than 5,000m3/d were in operation in the region, demonstrating the maturity of the technology. This article presents a review of these large MBR plants, not only in terms of market expectation, but also with regards to specific design considerations such as filtration flux, filtration layout, plant ‘retrofit’ and the inclusion of primary clarification. Due to the low operation costs (energy demand) as compared to side-stream membranes, submerged low-pressure filtration technologies will remain the standard for large MBR applications in the near future. At the time of the study, all the plants within this size segment in Europe were equipped by the two MBR filtration leaders GE/Zenon and Kubota, but other technologies should penetrate this market segment in the coming years.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.021
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0210.002

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 head0.238
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), 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

Citations29
Published2009
Admission routes1
Has abstractno

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