The power of computational thermochemistry in high-temperature process design and optimization: Part 2 – Pyrometallurgical process modeling using FactFlow
Bibliographic record
Abstract
Computational thermochemistry is an essential tool when it comes to the design of new industrial pyrometallurgical processes. It also enables the optimization of existing processes by analyzing the effect of various operating conditions on key indicators such as the metal recovery, the product composition, the direct emissions and the process overall energy balance. The modeling of these complex processes requires the use of multiple streams and equilibrium reactors in order to perform a large series of thermodynamic calculations. It also needs to account for the kinetic limitations of key chemical reactions. Current thermochemical software restricts users to single equilibrium reactor calculations or necessitates advanced programming knowledge to build customized pyrometallurgical processes. In this work, we introduce a new process simulation interface called FactFlow, a multi-stream/multi-unit process simulator embedded in the FactSage package. It offers an intuitive and efficient interface for handling streams, performing equilibrium calculations and allowing the use of stream recycling loops. It also uses the extensive thermodynamic databases available in FactSage to describe the energetics of oxides, sulfides, carbides, salts and metallic phases. This new process simulator interface enables the solving of mass and energy balances of a wide range of pyrometallurgical processes related to the primary production of iron and ferroalloys, copper, titanium and more. In this work, this new interface is used to describe four pyrometallurgical processes, i.e. (i) ferrosilicon alloy production using a submerged arc furnace, (ii) the primary production of copper and the impact of E-waste recycling using a Noranda-like process, (iii) the primary titanium production via the Kroll process, and (iv) the production of direct reduction iron ore pellets via the MIDREX process. Results of the simulations performed in this work are systematically compared to data available in the literature.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".