COP Evaluation for a Membrane Liquid Desiccant Air Conditioning System Using Four Different Heating Equipment
Bibliographic record
Abstract
Liquid desiccant air conditioning (LDAC) is a promising technology in terms of energy efficiency, comfort and indoor air quality. The major components of a LDAC system are the dehumidifier and regenerator. The most commonly used design of dehumidifiers/regenerators is the packed-bed, which might result in the entrainment of desiccant droplets in air streams. A promising solution for the entrainment of desiccant droplets in air streams is to use a liquid-to-air membrane energy exchanger (LAMEE) as the dehumidifier/regenerator. A membrane LDAC system, which uses two LAMEEs as the dehumidifier and regenerator, is investigated in this paper. The operation of a LDAC system requires the continuous supply of heating and cooling energy to the desiccant solution. In this study, the COPs of four membrane LDAC systems are evaluated when four different heating equipment are used to provide the solution heating loads as follows: a gas boiler, a solar thermal system, a heating heat pump, and the condenser of a solution cooling heat pump. The COPs of the four systems studied are evaluated under wide ranges of six design/operating parameters as follows: ambient air temperature (Tamb) and humidity ratio (Wamb), number of heat transfer units (NTU), solution-to-air heat capacity ratio (Cr*), and solution inlet temperatures to dehumidifier (Tsol,deh,in) and regenerator (Tsol,reg,in). TRNSYS and CYCLE_D programs are used in this paper to simulate the performances of different systems studied. Results show that the membrane LDAC system which uses a single heat pump to provide the solution heating and
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".