Reactor network modelling for biomass-fueled chemical-looping gasification and combustion processes
Bibliographic record
Abstract
A reactor network was developed to predict the performance of biomass-fueled chemical-looping gasification (CLG) and chemical-looping combustion (CLC) in packed beds. The reactor network consists of a combination of continuous stirred-tank reactors, plug flow reactors, and packed bed reactor zones. The model was developed and validated using experimental data under both CLG and CLC conditions, as well as a sensitivity analysis. Using the validated model, a variety of oxygen carrier (OC) bed lengths and locations were assessed to determine the resulting impact on the CLG and CLC performance. For CLG, the highest gasification efficiency (75.1%) occurred with an OC/biomass ratio of 0.25 combined with a steam/biomass ratio of 1, with the OC placed near the reactor inlet. The resulting gasification efficiency was of similar magnitude to that reported in experimental data. For CLC, a fully packed bed with steam as the inlet gas resulted in the highest outlet CO2 fraction, with an outlet stream that consisted of 95% CO2 and H2O. This reactor design improved the outlet CO2 purity by an order of magnitude in comparison with experimental data; thus demonstrating their potential and advancing the commercial adoption of these emerging technologies. The model was also used to predict the dynamic behaviour of the system and to determine the most suitable time to end the reduction stage, while maintaining a high CO2 fraction in the recovered gas. These design strategies can be implemented to facilitate biomass-fueled energy generation by improving the sustainability of the gasification and combustion systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".