Dehumidification performance investigation of run-around membrane energy exchanger system
Bibliographic record
Abstract
Liquid-to-air membrane energy exchanger is a novel membrane base energy exchanger, which allows both heat and moisture transfer between air and a salt solution. It uses semi-permeable membrane to eliminate entrainment of liquid desiccant as aerosols in air stream and allow simultaneous heat and moisture transfer between salt solution flow and airflow. The heat and mass transfer performance of a single liquid-to-air membrane energy exchanger is significantly dependent on two dimensionless parameters. They are the number of heat transfer units (NTU) and the ratio of heat capacity rates between solution flow and air flow (Cr*). The liquid-to-air membrane energy exchangers can also be applied in a run-around membrane energy exchanger system, which is mainly comprised of two liquid-to-air membrane energy exchangers and a closed loop of aqueous desiccant solution and used as a passive energy recovery system to recover the energy (both heat and moisture) from the exhaust air to precondition the supply air in air conditioning systems. In this study the dehumidification capacity of a run-around membrane energy exchanger is investigated numerically at different exhaust air temperatures and Cr* values. Increasing the exhaust air temperature or the Cr* would enhance the dehumidification capacity of the a run-around membrane energy exchanger system under Cr*?1, but the improvement is limited. The dehumidification capacity at low Cr* is much lower than that under the optimal Cr* value (Cr*=3.2) where the maximum latent effectiveness is obtained.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".