Characterization of the Evolution of Crystallization Fouling in Membranes
Bibliographic record
Abstract
Abstract Liquid-to-air membrane energy exchangers (LAMEEs) are promising in heating, ventilating, and air-conditioning applications because they are able to use semipermeable membranes to transfer heat and moisture between air and liquid desiccant streams. However, the development of crystallization fouling in membranes may pose a great risk to the long-term performance of LAMEEs. The main aim of this paper is to characterize the evolution of crystallization fouling in membranes through the use of both noninvasive and invasive methods. Noninvasive methods are used to study the development of fouling in the LAMEE by monitoring the changes in moisture flux through the membrane and overall moisture-transfer resistance of the LAMEE. On the other hand, invasive methods are implemented to characterize fouled membranes by using optical microscopy and scanning electron microscopy (SEM) to depict the morphology of crystal deposits and energy-dispersive X-ray spectroscopy (EDX) to identify the composition of the deposits. Experiments are performed by using air to dehydrate MgCl2(aq) at two operating conditions of low and high fouling rates. The results show that the moisture flux decreases and the moisture-transfer resistance increases more considerably during the test at the high fouling rate than in the test at the low fouling rate. SEM micrographs show that cake crystal deposits cover the membrane surface in the test at the high fouling rate, whereas only few crystal particles are observed on the membrane in the test at the low fouling rate. Furthermore, the crystal deposits undergo more structural changes in the tests at the high fouling rate than in the tests at the low fouling rate, possibly because of the higher moisture transfer rate through the membrane in the tests at the high fouling rate. Finally, the SEM?EDX analysis confirms that the crystal deposits primarily consist of Mg, Cl, and O elements.
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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 | 0.001 |
| 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 | 0.000 | 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".