Modeling the Coal Tar Pitch Primary Carbonization Process
Bibliographic record
Abstract
The properties of the carbon materials obtained as the final product of coal tar pitch carbonization process are a consequence of the type of chemical and physical phenomena occurring through the process. A new simplified approach for modeling of the primary carbonization is presented to provide the semi-quantitative knowledge about the process useful for improving the efficiency of the industries that deal with this process. The proposed approach is based on defining thermodynamic and kinetic equations simply representing numerous phenomena happening during primary carbonization. Partial pressures of emitted volatiles in a simple pitch system are studied. The model enables estimating the mass and enthalpy changes of pitch through thermal treatment consistent with experimental data for mass losses of pitch heat treated up to 550 °C. Application of the model to describe molecular weight distribution changes of pitch during primary carbonization is demonstrated, showing a good agreement between the presented results and the investigations reported by Greinke. For the first time, the effect of important parameters in pitch carbonization, such as the heating rate of the pitch and the carrier gas flow rate, on the emission rate of volatiles is successfully modeled. The present model is well able to estimate the energy requirement for thermal treatment of pitch up to 350 °C.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".