Development of a Fuzzy‐based Risk Assessment Model for Process Engineering
Bibliographic record
Abstract
Global interest in sustainable development has increased emphasis on the social, environmental, and economic impacts of products and processes. Ample evidence is available on the triple bottom line impacts of engineered systems creating ecological and social risks. Hence, the management of triple bottom line based risks should be a key consideration in operational management. The risk matrix has been commonly used to prioritize the risks associated with the prevention and remediation of environmental damage. Despite its popularity, however, data uncertainty is the main challenge associated with the risk matrix. The published literature identifies imprecision of risk level and absence of data as added challenges. A fuzzy logic (FL) based risk matrix model (RMM) has been developed to overcome the aforementioned challenge. The proposed RMM adopts a unique approach, integrating frequency of the risk consequence and impact of the corresponding consequence. The frequency of risk consequence is based on four factors: (i) the frequency of risk events, (ii) exposure to risk events, (iii) the probability of failure on demand (PFD) of the safeguards, and (iv) the vulnerability of safeguards. The consequence impacts involve the impact on humans, the environment, properties, and the reputation of the industry. To facilitate the computations of RMM, a Simulink-MATLAB model was developed and demonstrated by using a case study of a reboiler oven in the petroleum industry. The study revealed that the results of the new RMM can be more reliable than traditional risk matrices.
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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 | 0.001 |
| 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) | 0.001 | 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".