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Record W4413489817 · doi:10.1016/j.jobe.2025.113796

Impact of side stream crossflow filtration on water quality in a HVAC circuit

2025· article· en· W4413489817 on OpenAlexafffund
Ambroise Bellamy, Vaishali Ashok, Étienne Robert, Alain Silverwood, Dominique Claveau-Mallet, Émilie Bédard

Bibliographic record

VenueJournal of Building Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsPolytechnique Montréal
FundersMitacs
KeywordsHVACFiltration (mathematics)Environmental scienceQuality (philosophy)Water qualityEnvironmental engineeringEngineeringMechanical engineeringPhysicsMathematicsAir conditioningStatistics

Abstract

fetched live from OpenAlex

This study evaluates the impact of a VORTISAND® Crossflow Microsand Filter (CMF) on the water quality and operational efficiency of a cooling tower system in a large hospital building. Data was collected over 18 months, comparing periods when the filter was active versus inactive. Results demonstrated that the filter reduces turbidity, total suspended solids, and particle counts by 20%, 25-30% and 69%, respectively. The filter was effective particularly for particles >1 μm . Filtration also decreased the concentration of iron and calcium ions by 15% and 25%, indicating its effectiveness in managing particulate and scaling components. While the filter improves water quality and stabilizes system operations, it increases water consumption by 20.7 % due to backwashing. These findings highlight the potential of integrating physical filtration into cooling tower management to optimize performance and reduce chemical use, although further strategies are needed to minimize water losses. This study offered a unique opportunity to successfully shut down the CMF in a full-scale operating system with high maintenance standards and assess the actual impact of the filtration on water quality and operability of the HVAC system.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score0.391

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 head0.012
GPT teacher head0.288
Teacher spread0.275 · 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
Published2025
Admission routes2
Has abstractyes

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