Gaseous Contaminant Transfer in Membrane Energy Exchangers
Bibliographic record
Abstract
Membrane-based air-to-air energy exchangers (M-AAEEs) reduce the energy required for conditioning building ventilation air by transferring heat and moisture between the building exhaust and ventilation air. However, M-AAEEs may also contaminate the ventilation air due to the transfer of contaminants from the stale exhaust air leaving the building to the fresh ventilation air being supplied to the building. It is important to quantify this transfer to assess its impact on indoor air quality. Hence, the main objective of this thesis is to quantify contaminant transfer in M-AAEEs using test methods available in the literature. \n\nA test facility was developed, and experiments were performed to quantify the contaminant transfer for different air flow rates, pressure conditions, contaminants and membrane types. Contaminant transfer was quantified using a parameter called the exhaust contaminant transfer ratio (ECTR), which gives the fraction of the contaminants that transferred from the exhaust air to the ventilation air. Furthermore, a theoretical model was developed to predict contaminant transfer through the membranes. The results from the experiments and the theory were in good agreement within uncertainty limits. The experimental uncertainty in ECTR was between ± 0.5% and ± 2.7%.\n\nContaminants with lower molecular weight and smaller size (i.e., higher diffusivity) tend to have higher transfer through the membranes considered in this study. As a result, the inert tracer gas test, which is recommended in current energy exchanger standards to determine leakage in M-AEEs, may not represent the transfer of many common indoor contaminants. Moreover, the theoretical model presented in this study can be used to estimate contaminant transfer if the moisture transfer rate through the membrane is known.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".