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Record W2045755987 · doi:10.2166/wst.2007.274

A biofilm model for engineering design

2007· article· en· W2045755987 on OpenAlexaff
I. Takács, Christopher M. Bye, K. Chapman, Peter Dold, P.M. Fairlamb, Richard M. Jones

Bibliographic record

VenueWater Science & Technology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsEnviroSim (Canada)
Fundersnot available
KeywordsActivated sludge modelSolverBioreactorSewage treatmentBiochemical engineeringProcess (computing)BiofilmProcess engineeringWastewaterEngineering design processAnoxic watersActivated sludgeEngineeringEnvironmental scienceEnvironmental engineeringComputer scienceChemistryMechanical engineering

Abstract

fetched live from OpenAlex

A biofilm model is presented for process engineering purposes--wastewater treatment plant design, upgrade and optimisation. The model belongs in the 1D dynamic layered biofilm model category, with modifications that allow it to be used with one parameter set for a large range of process situations. The biofilm model is integrated with a general activated sludge/anaerobic digestion model combined with a chemical equilibrium, precipitation and pH module. This allows the model to simulate the complex interactions that occur in the aerobic, anoxic and anaerobic layers of the biofilm. The model has been tested and is shown to match a variety of design guidelines, as well as experimental results from batch testing and full-scale plant operation. Both moving bed bioreactors (MBBR) and integrated fixed film activated sludge (IFAS) systems were simulated using the same model and parameter set. A new steady-state solver generates fast solutions and allows interactive design work with the complex model.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0120.004

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 head0.016
GPT teacher head0.222
Teacher spread0.206 · 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 designSimulation or modeling
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

Citations25
Published2007
Admission routes1
Has abstractyes

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