Application of FactSage thermodynamic modeling of recycled slags (Al<sub>2</sub>O<sub>3</sub>–CaO–FeO–Fe<sub>2</sub>O<sub>3</sub>–SiO<sub>2</sub>–PbO–ZnO) in the treatment of wastes from end-of-life-vehicles
Bibliographic record
Abstract
Abstract The FactSage thermochemical software and databases calculates complex, multi-component, multi-phase equilibria involving simultaneously slag, metal, ceramic, and gas phases, over wide ranges of temperature, oxygen potential and pressure. The databases are automatically accessed by the software and the outputs of the Gibbs free energy minimization calculations can be presented in ways that are convenient to engineering practice, and as functions of key process variables. The new thermodynamic databases for slag and solid oxide phases in the Al 2 O 3 – CaO – FeO – Fe 2 O 3 – SiO 2 – PbO – ZnO system have been developed by critical evaluation/optimization of all available phase equilibrium and thermodynamic data. By means of the optimization process, model parameters are found which reproduce all thermodynamic and phase equilibrium data within experimental error limits. Furthermore, the models permit extrapolation into regions of temperature and composition where data are not available. Phase equilibrium calculations have been undertaken, that are of interest in the thermal treatment of Automobile Shredder Residue (ASR) in the Al 2 O 3 – CaO – FeO – Fe 2 O 3 – SiO 2 – PbO – ZnO system. This 7-component system represents only the major components of ASR. There are at least a dozen other important components in ASR not to mention the organic matter. The operating conditions deviate from thermodynamic equilibrium and the oxygen potential during the treatment of wastes is not well established. Consequently, the calculated diagrams are intended only to give an idea to industrial engineers about the trends in the liquidus temperature, extent of crystallization and partial pressures of volatile components as functions of temperature, composition and oxygen potential. These diagrams may also help to identify the process variables that are most important for industrial practice significantly reducing the amount of experimental work that has to be done to optimize the operations.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".