Study on simulation of pulverized coal gasification process in the GSP gasifier
Bibliographic record
Abstract
A model of entrained‐flow coal gasifier (1920 t/d and single nozzle) was established to simulate the gasification process of pulverized coal from Ningxia of China on the basis of ANSYS Fluent. In order to simulate the characteristics of the coal gasification process, the Lagrange model was adopted to obtain properties of particles, and the standard k‐ϵ turbulence model was applied to the gas phase flow. Meanwhile, the species transport model and modified eddy‐dissipation model (EDM) were used to simulate the species transport and gas phase reaction, respectively. Radiation heat transfer was modelled with the P1 model. The simulation results were in good agreement with the actual production data. In addition, the effects of the pressure, temperature, oxygen‐coal ratio, and steam‐coal ratio on the gasification process were studied using this model. It was found that increasing the gasification pressure had little effect on the composition of outlet gas. The concentration of CO and H2 increased with the increase of temperature. The best oxygen‐coal ratio range was 0.70–0.85 for this gasifier and coal sample. The steam‐coal ratio was decided by the actual operating conditions of the gasification. The simulation of the coal gasification process can predict the details of flow field, temperature field, velocity field, and concentration field in the gasifier. It is helpful to clearly understand coal gasification behaviour inside the gasifier so as to better understand the actual gasification process. Furthermore the simulation method is practicable in the gasifier design and structure optimization, and also supplies some useful data to assist the production operation.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".