An insight into hybrid membrane‐based air conditioning system performance using gray relational analysis methods: Structural versus operational parameters
Bibliographic record
Abstract
Abstract Various parameters have an impact on the fresh air temperature and input power of a hybrid membrane‐based air conditioning (HMBAC) system, such as pressure ratio, membrane selectivity, membrane permeance, membrane area, air flowrate of the dehumidification unit, and module length, number of fibers, and fiber outer diameter of the humidification unit, which comprise both operational and structural parameters. Therefore, in this study, the effects of these parameters on the system performance are examined separately based on a statistical approach. The importance order of each parameter and its contribution ratio are determined by using Taguchi method and ANOVA analysis. The optimum level for each input parameter is determined using statistical analysis for the fresh air temperature and input power. Then, for the simultaneous minimization of fresh air temperature and input power Taguchi‐gray relational grade (GRG) is used. GRG revealed that membrane selectivity, pressure ratio, and membrane permeance with the contribution ratio of 37.87%, 32.51%, and 10.55%, respectively, are the most critical parameters of the multiperformance of an HMBAC system. Interestingly dehumidification from a humid airflow can be more effective when membranes with low selectivity and high permeability are used. In conclusion, the optimization outputs disclosed that both structural and operational parameters have significant effects on the performance of the HMBAC system and this optimization can be a helpful tool for designing a HMBAC systems to cover the optimal conditions to develop a sustainable membrane‐based air conditioning system.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".