Simulation of a bubbling fluidized bed reactor for phosphogypsum decomposition with carbon monoxide
Bibliographic record
Abstract
Abstract Phosphogypsum (PG), a by‐product of the phosphoric acid industry and rich in calcium sulphate (CaSO 4 ), has an unfavourable environmental impact, making its management one of the crucial issues of phosphoric acid production. As a result, PG production is integrated with or without treatment in many industries, such as agriculture, building materials, and sulphuric acid. In the latter application, carbon monoxide (CO) is used to decompose PG to extract the sulphur according to a two‐parallel reaction producing calcium sulphide (CaS) and calcium monoxide (CaO) in the solid phase. To depict the effect of the solid temperature ( T ), CO partial pressure ( P CO ), inlet gas velocity ( U 0 ), residence time (), particle size (PS), and solid exchange coefficient () on the overall fluidized bed reactor performance, a simulation study was performed according to the dynamic two‐phase model. The results show that CO partial pressure, solid residence time, particle size, and inlet gas velocity have the most significant effect on the PG conversion. On the other hand, the temperature and partial pressure of CO have the most critical effect on product selectivity. The concentration profiles of both reactants CaSO 4 and CO reveal that the reaction in the emulsion phase is faster than in the bubble phase due to the good mixing in this phase.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".