Multi‐objective optimization of drainage‐plates in wave‐plate mist eliminators using experiment and data‐driven modelling for lower water loss and energy requirement
Bibliographic record
Abstract
Abstract Wave‐plate mist eliminators are widely employed as gas–liquid separation devices to prevent the liquid escaping from thermal power plants or other cooling towers. In this study, the wave‐plate mist eliminator with drainage plates was numerically analyzed and the effects between geometrical variables on two objectives, namely, pressure drop (Δ P ) and separation efficiency ( η ), were revealed. Plate spacing, width, and length, as well as the relative position of the drainage plate, were thoroughly investigated. A combined strategy was developed for multi‐objective optimization of the wave‐plate mist eliminator by integrating computational fluid dynamics (CFD) simulation, response surface methodology (RSM), non‐dominated sorting genetic algorithm‐II (NSGA‐II), and a technique for order of preference by similarity to ideal solution (TOPSIS) method. The results demonstrated that the relative position of drainage plates has a greater impact on the overall performance, whereas the width of drainage plates has the minimum effect. With the implementation of NSGA‐II and the TOPSIS method, an optimal solution for the design of the mist eliminator was obtained. After comparing with the baseline case, the optimized case presents promising characteristics with high separation efficiency (enhanced by 3.6%~9.06%) and a low energy consumption coefficient (reduced by 72.30% at η = 45%).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".