Study on the pyrolysis characteristics of rolling oil sludge in the iron and steel industry
Bibliographic record
Abstract
Abstract Pyrolysis is an effective technology that can recover energy and resources from rolling oil sludge (ROS), a hazardous waste. In this work, thermogravimetric analysis experiments were used to investigate the variation in the thermal weight loss curve of the ROS at different heating rates and then assesses the pyrolysis characteristics of the ROS. Based on the Ozawa–Flynn–Wall (OFW), Friedman (FM), and Kissinger–Akahira–Sunose (KAS) models, the characteristic reaction kinetics of the ROS pyrolysis process were calculated. The apparent activation energy trends calculated by the three models were the same, and in all cases, the apparent activation energy increased with increasing conversion ratio. The thermodynamic function of the pyrolysis process at different heating rates was calculated via the OFW model, and A, ΔH, and ΔS tended to increase with increasing conversion ratio. An experimental tubular furnace study revealed that an increase in the pyrolysis temperature of the ROS had a positive effect on the combustible component content, reducing the component content, iron grade, and metallization ratio of the pyrolysis residue of the pyrolysis gas. Analysis of the pyrolysis behaviour of the ROS revealed that the ROS has the potential for resource utilization.
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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) | 0.000 | 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) | 0.000 | 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 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".