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Record W1967071983 · doi:10.1504/ijpse.2009.028000

Optimal design of a hybrid air stripping/pervapouration system for removal of multicomponent VOCs from groundwater

2009· article· en· W1967071983 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Process Systems Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsGroundwaterStripping (fiber)Environmental scienceEnvironmental engineeringWaste managementAir strippingEngineeringGeotechnical engineeringElectrical engineeringWastewater

Abstract

fetched live from OpenAlex

Groundwater streams contaminated with volatile organic compounds (VOCs) require proper treatment to comply with drinking requirement restrictions. Air stripping and pervapouration are two common treatment technologies for groundwater contaminated with VOCs. An attractive alternative for VOC treatment may be to combine these two technologies together within the same circuit since this offers potential advantages over the operation of either of these systems alone. The current study focuses on the important issue of determining in an objective and systematic manner how to combine these two technologies to meet the drinking water requirements in the most cost-effective manner. Superstructure optimisation is the framework for hybridisation investigated in this study to determine the optimal treatment network and operating conditions for the treatment units to achieve desired water qualities. Two case studies illustrating the proposed approach are presented and the sensitivity of their optimal solutions to perturbations in certain operating conditions is discussed.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.554
Threshold uncertainty score0.612

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 head0.016
GPT teacher head0.238
Teacher spread0.222 · 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