Complex network analysis for accident causes modelling to enhance process safety in chemical enterprises
Bibliographic record
Abstract
Abstract Hazardous chemicals often cause catastrophic accidents, and accidents often result from intricate interactions among various causes. Due to the varying risk factors in different areas of chemical enterprises, to achieve more precise prevention, a more detailed study of the accident risk factors in each area is necessary. Therefore, this study focuses on analyzing critical accident causes and their interrelationships in different functional areas of chemical enterprises to enhance process safety by using a complex network model. Based on 90 accident information, complex network models are constructed for hazardous chemical warehouse areas (HCWAs), tank farm areas (TFAs), and production areas (PAs). Subsequently, a topological analysis of the complex network models is conducted. Based on the PageRank algorithm, 13 critical nodes are identified for HCWAs, while 14 for TFAs and 13 for PAs. Node degree analysis with confidence quantifies mutual influences, forming critical accident causal links for each area. The research results offer decision support for precise accident risk control, aiding in reducing future accidents and improving process system safety in chemical enterprises.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".