Bibliographic record
Abstract
This thesis investigates the risk of offshore oil and gas processing equipment operating in a harsh environment. It comprises of two major studies which form the core of journal papers submitted for publication. The first study presented a new risk assessment methodology with applications to fire scenarios in compressor and heat exchanger units. A sensitivity analysis was conducted to identify the critical components, their interdependence, and importance in causing failure. In the second study, a risk assessment approach is proposed that demonstrates how process system failure risk could be assessed in the absence of complete data. The approach also highlighted the importance of interdependence of the failure causation factors. The Bayesian Network (BN) is used in the study to capture interdependence of and uncertainty the variables. Noisy-OR and Leaky Noisy-OR logics are used to improve uncertainty-handling capacity and overcome the data requirement. Application of the proposed approach is demonstrated on a subsea pipeline failure scenario. As a first step, a Bowtie (BT) was developed which captures all the possible failure causes of a leak and shows the potential consequences of a leak in the subsea pipeline. The BT was then mapped to a BN for OR, Noisy-OR and Leaky Noisy-OR logics. Failure probabilities of Subsea Pipeline and its Safety Barriers were calculated with Bow-tie and Bayesian Network for different Logics. Finally, importance analysis was performed for 21 basic events using OR, Noisy- OR and Leaky Noisy OR Logics to determine safety critical elements. In Summary, this thesis provides scientifically sound and applied approaches to conduct risk assessment of process components with limited data. Applications of these approaches demonstrated on different case studies. Use of the proposed approaches would help better understanding of failure and hence improving safety of process system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".