Multi‐object optimization study on the performance of gas cyclones based on heterogeneous condensation and turbulent agglomeration
Bibliographic record
Abstract
Abstract Improving the separation efficiency of fine particles becomes more and more critical as environmental pollution aggravates. This study aims to investigate the effects of four key parameters on the performance of gas cyclones, including cyclone body height, particle concentration, initial supersaturation degree, and inlet temperature. Then, the two‐way coupling numerical model, in which is the process of heterogeneous condensation and agglomeration for insoluble fine particles, was achieved by user defined function. On this basis, the response surface analysis method and multi‐objective genetic algorithm were adopted to optimize the cyclone. The results show that when the particle concentration is less than 1000 mg/m 3 , the separation efficiency can reach above 95%. The initial supersaturation degree has the greatest effect on the separation efficiency and vapour consumption rate, while the cyclone body height is the most critical factor on the pressure drop. As the particle concentration increases, the separation efficiency decreases at first and then keeps almost stable. With the increase of inlet temperature, the separation efficiency is enhanced, and the pressure drop reduces. These research results can provide important guidance for the optimization and engineering application of this technology.
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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 | 0.000 |
| 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".