Passive Fire Protection Considerations for Oil Sands Applications
Bibliographic record
Abstract
Abstract Oil sands projects have unique features due to process conditions and environmental factors. Process safety risks such as fire hazards are typically mitigated through application of Passive Fire Protection (PFP). Cementitious and intumescent PFP types are commonly used to protect plant structures. Cladding type or flexible jackets are more common for fire protection of piping systems and safety critical components including equipment, valves and instrumentation systems. Due to weather conditions, application of PFP products at plant site or modular construction requiring transportation over long distances may pose risks for major projects. This study aims to share lessons learnt and recommendations for selection of PFP types for oil sands projects and facilities with similar environmental and process conditions. Critical considerations for selection of PFP type in the oil sands fields are discussed in this study with respect to abrasion resistance, short- and long- term integrity, and corrosion under insulation risks. This study provides case studies and recommendations for PFP selection and application at cold regions where the facility has high momentum jet fire risks similar to those at oil sands facilities. Also, methods to optimize PFP application are discussed so that required coating quantities can be minimized and potential integrity issues can be mitigated. This approach is expected to be beneficial for reducing coating related Capital Expenses (CAPEX) and Operational Expenses (OPEX) at oil sands and other process plants where there are fire risks.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".