Process simulation on oxy‐fuel combustion of coal and biomass in a circulating fluidized bed
Bibliographic record
Abstract
Abstract Appropriately evaluating the gas–solid hydrodynamics and reaction kinetics of reactors within process simulation approach can provide more accurate and comprehensive techno‐economic and environmental assessments, as well as more effective design and optimization for new processes and technologies. In this study, a one‐dimensional process simulation of biomass and coal oxy‐co‐firing in a 100 kWth circulating fluidized bed was developed, with the models for gas–solid hydrodynamics and synergistic reactions of two fuels being established in Aspen Plus software. The effects of fuel properties, gas atmosphere, and operating parameters on the combustion process, flue gas products, and heat distributions in the bed were studied. Results show that the proposed process models can successfully describe the oxy‐co‐firing of coal and biomass in the furnace including the pyrolysis and the combustions of gaseous volatile and char. Increasing the oxygen concentration and biomass blending ratio will improve the combustions in dense phase region and obviously increase the heat release in this region. More heating surfaces should be arranged in the dense phase region when retrofitting an existing circulating fluidized bed (CFB) boiler with air combustion to be the oxy‐fuel one. Additionally, the increase of oxygen concentration could reduce the emissions of pollutants such as CO and NOX, while the addition of biomass may bring a slight increase in NOX emission.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".