Simulation analysis of gas–solid flow characteristics and water evaporation in flue gas semi‐dry desulphurization process based on <scp>CPFD</scp> method
Bibliographic record
Abstract
Abstract The gas–solids flow in an industrial‐scale semi‐dry method desulphurization tower is simulated by the computational particle fluid dynamics (CPFD) approach. Compared with previous studies on desulphurization towers, this study focuses on analyzing particle distribution characteristics such as particle volume fraction, temperature distribution, and residence time. The simulation fully considered the particle–fluid, particle–particle, and particle–wall interactions in the desulphurization tower. Based on these considerations, the effects of flue gas inlet velocity and temperature on the gas–solid distribution characteristics of the desulphurization tower are simulated. An optimization scheme for adjusting the gas–solid flow in the desulphurization tower is proposed. The research results show that the error between the CPFD simulation data and experimental data is small and the changing trend is consistent. The particles in the bed of the desulphurization tower show a typical core–annulus flow. The distribution of gas and particles in the bed has a serious deviation with the increase of the flue gas inlet velocity and temperature. As the axial height of the desulphurization tower increases, the flue gas velocity, temperature, particle concentration, and water vapour distribution in the bed become more uniform. The relatively stable operating conditions for the gas–solid flow in the desulphurization tower is that the flue gas inlet velocity and temperature are 15 m/s and 393 K, respectively. Under these operating conditions, the pressure loss caused by the venturi accounted for 73.6% of the total pressure loss of the desulphurization tower. When the particle radius is between 0–150 μm, the particle size and the flue gas inlet velocity have the greatest influence on the particle residence time. Finally, the distribution of gas and particles before and after the adjustment of the desulphurization tower is compared, which showed that adjusting the bottom structure of the desulphurization tower could optimize the gas–solid flow.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".