Loss functions and their applications in process safety assessment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Process deviations, along with failure of control systems and protection layers, result in safety and quality loss in plant operations. This article proposes an operational risk‐based warning system design methodology based on overall system loss. Loss functions (LFs) are used to define the relationship between process deviations and system loss. For this purpose, properties associated with quadratic LF and a set of inverted probability LFs are investigated and compared. The results suggest that LFs can be used in a novel way to assess operational stability and system safety. The proposed consequence assessment methodology using LFs is then incorporated into a risk‐based warning system design model to analyze warnings associated with process deviations. A simulated case study is presented to demonstrate potential application of the proposed methodology; the study examines the response to a temperature surge for a reactor system. © 2014 American Institute of Chemical Engineers Process Saf Prog 33: 285–291, 2014
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it