Reactor network modelling for biomass-fueled chemical-looping gasification and combustion processes
Bibliographic record
Abstract
• A reactor network model is proposed for packed bed biomass-fueled CLG and CLC. • Model is validated using literature data under both CLG and CLC conditions. • Biomass-fueled CLC in a PBR produces an outlet gas stream with good CO 2 purity. • Improved performance of CLG using a small OC bed near the inlet with S/B ratio of 1. • Best CLC performance for reactor fully packed with OC using steam as the inlet gas. 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 CO 2 fraction, with an outlet stream that consisted of 95% CO 2 and H 2 O. This reactor design improved the outlet CO 2 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 CO 2 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".