Probabilistic assessment of the safety profile of the <scp>Fischer–Tropsch</scp> process with a supercritical solvent
Bibliographic record
Abstract
Abstract Inherent safety assessment during the design stage of chemical processes is typically conducted based on average values for design parameters. Under those conditions, the single‐point deterministic process performance assessment may be affected by the phenomenon known as the ‘flaw of averages’ in the presence of irreducible sources of uncertainty (performance evaluated at average conditions does not represent average performance). In this work, an inherent process safety assessment developed under a probabilistic formulation is presented. An evaluation of the proposed approach is performed in the case of a gas‐to‐liquid process system using a supercritical solvent for Fischer–Tropsch reactor systems. The pertinent uncertainty analysis is carried out using Monte Carlo simulation techniques to account for the propagation of uncertainty through the inherent process safety model and the derivation of probability distribution profiles for the associated metrics, thus statistically characterizing ranges of potential performance outcomes. The response variables were the autothermal reactor and the syngas flows. The results show that the input variables associated to the autothermal flow potentially generate the most hazardous conditions for the process. The results also show how the metrics are affected when uncertainty is explicitly taken into account at the design stage of the process, offering a more nuanced assessment and characterization of the inherent process safety profile.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".