Multi‐object optimization study on the performance of gas cyclones based on heterogeneous condensation and turbulent agglomeration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it