Pyrolysis and Volatile Evolution Behaviors of Cold-Rolling Oily Sludge
Bibliographic record
Abstract
Cold-rolling oily sludge contains high amounts of oil and iron resources that can be recycled by pyrolysis. We investigated the pyrolysis behavior and volatile products of oily sludge by thermogravimetric analysis (TG) coupled with Fourier transform infrared spectroscopy (FTIR) and a pyrolyzer (PY) coupled with gas chromatography/mass spectrometry (GC/MS). The pyrolysis process was divided into three stages: H2O drying and CO2 desorption at low temperatures (below 393 K); the volatilization of low-molecular-weight organics and the covalent bond cleavage of C=C, C-O, and C-H in the medium-molecular-weight organics at medium temperatures (393–844 K); and chain scission of the high-molecular-weight organics and reduction of iron oxides by CO at high temperatures (above 844 K). The weight losses of oily sludge in the three stages were 0.4 wt %, 47.9 wt %, and 14.7 wt %, respectively. According to the kinetic models, stage 2 and stage 3 could be described with the second-order and third-order reaction models, and their activation energies were 40.22 kJ/mol and 214.99 kJ/mol, respectively. The compounds in the volatile products were identified by FTIR and GC/MS. The organics in the volatile products from stage 2 pyrolysis mainly consisted of aliphatic hydrocarbons, fatty acids, esters, ketones, and nitrogen compounds, while the volatile products from stage 3 predominantly contained aliphatic hydrocarbons, mononuclear aromatic hydrocarbons, and small amounts of nitrogen compounds and CO, suggesting the occurrence of chain scission of heavy organics.
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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 | 0.000 | 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) | 0.000 | 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".