Process analysis of a two‐stage fluidized bed gasification system with and without pre‐drying of high‐water content coal
Bibliographic record
Abstract
Abstract In order to investigate the gasification performance of coal with high water content, a two‐stage fluidized bed gasification (TSFBG) system was systematically simulated by Aspen Plus to identify the effect of pre‐drying for coal with its initial water content varying from 10‐65 wt% on gasification performance, particularly energy efficiency. The results show that the energy efficiency based on lower heating value (LHV) (ηLHV) and higher heating value (HHV) (ηHHV) of feed coal are about 1.5%‐7% and 1.5%‐5% higher when coal is fed to the system directly without pre‐drying. For the TSFBG system, the higher the water content, the greater the energy efficiency reduction by pre‐drying. The analysis of energy allocations reveals that heat loss due to the pre‐drying of coal is mainly responsible for the decrease of energy efficiency in operations with pre‐drying. With an increase in the initial water content from 10 to 65 wt%, the ηLHV of the TSFBG system without the pre‐drying of coal using air/steam as gasification agent reaches its maximum of about 91% at an initial water content of 26 wt%. The ηLHV and ηHHV of the TSFBG system using oxygen/steam as gasification agent increases energy efficiency by about 1%‐2% compared to that using air/steam. For TSFBG using air/steam to gasify coal without the pre‐drying, the preferred initial water content of coal is below 50 wt%.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".