Brown coal char CO<sub>2</sub>‐gasification kinetics with respect to the char structure part II: Kinetics and correlations
Bibliographic record
Abstract
Abstract Coal's physical structure is known to affect high‐temperature coal conversion processes such as gasification or combustion. This paper is Part II of the study of brown coal char structural changes during gasification. Firstly, German Lusatian brown coal was gasified in a laboratory‐scale fluidized bed reactor in CO2 within a temperature range of 800–950 °C at atmospheric pressure. Then, physical structure properties were extensively evaluated by means of various techniques. Char specific surface area and its changes during gasification reaction were evaluated using N2 and CO2 physical adsorption techniques. Adsorption isotherms were also interpreted employing unconventional methods in order to obtain the specific surface areas of pores of different sizes. Gasification kinetics were evaluated employing three widely applied kinetic models: the random pore model (RPM), the volume reaction model (VM), and the shrinking core model (SCM). Finally, the instantaneous gasification reaction rate was correlated with the char structural properties at the corresponding conversion degrees. The closest linear correlation appeared between the gasification reaction rate and the specific surface area of mesopores (as determined by N2 adsorption). Furthermore, correlations of the other structural properties with char conversion are provided. Observed structural changes were compared with the assumptions of the 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".