Performance Evaluation of Torrefaction Coupled with a Chemical Looping Gasification Process under Autothermal Conditions: Flexible Syngas Production from Biomass
Bibliographic record
Abstract
Herein, a coupled biomass torrefaction and chemical looping gasification (BTCLG) process was proposed, and process simulations were performed under autothermal conditions. The effects of operational parameters on product distribution with torrefaction temperatures from 240 to 300 °C, gasification temperatures from 700 to 880 °C, and steam/biomass (S/B) ratios from 0.5 to 1.5 were explored. The results showed that the process could stably operate under autothermal conditions. Torrefaction increased the syngas yield, heating value, and cold gas efficiency by 12.44, 5.11, and 36.73%, respectively. Moreover, it reduced the bed material circulation rate required for autothermal operation by 59.03%. At the torrefaction temperature of 240 °C, the syngas yield reached a maximum value of 0.78 Nm 3 /kg. Increases in the gasification temperature also positively impacted syngas production. However, the optimal gasification temperature was 810 °C based on the bed material circulation rate constraint. The syngas H 2 /CO ratio could be flexibly adjusted from 1.62 to 3.34 by changing the S/B ratio to meet downstream demands. Overall, the simulation results support the technical viability and demonstrate the excellent performance of the BTCLG process compared to the biomass CLG process.
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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) | 0.000 | 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) | 0.001 | 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".