Fluidized Bed Combustion of Natural Gas and other Hydrocarbons
Bibliographic record
Abstract
Abstract The sections in this article are Introduction Heterogeneous and Homogeneous Kinetics Mixing in Gas/Solid Fluidized Beds Mixing Fluidized Bed Combustion Fluidized Bed Combustion Modeling Heterogeneous and Homogeneous Kinetics Homogeneous Kinetics Global Homogeneous Combustion Kinetics Microkinetic Combustion Models ReducedGRIMechanism Combined Homogeneous and Heterogeneous Kinetics Inert Particles Kinetics for Methane Combustion in Inert Particles Combustion Catalysts Oxygen Carriers Mixing in Gas/Solid Fluidized Beds Superficial Gas Velocity and Bubble Size Particle Size Effect of Baffles Sparger Flow Pattern for Non‐Premixed Operation Methane Fluidized Bed Combustion Inert Particles Fluidized Bed Temperature Bubble Size and Air‐to‐Fuel Ratio Fluidization Regime COFormation NOxFormation Particle Type and Size Combustion Catalysts Oxygen Carriers/Chemical‐Looping Combustion Fluidized Bed Combustion Modeling Single‐Phase Models Two‐Phase Models Model ofDavidson andHarrison Bubbling Bed Models Bubble Assemblage Models Multiple Region Models Kinetic Models
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.007 | 0.002 |
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".