An inherently safer layout design for the liquefaction process of an FLNG plant
Bibliographic record
Abstract
Floating Liquefied Natural Gas (FLNG) facilities have limited space available and a high possibility of accidents occurring. The severity of consequences requires an inherently safer layout design. Scope of the liquefaction process requires to determine the size of utilities, operating costs, the deck area and the number of LNG trains. The layout of the liquefaction process plays a key role in defining operational and economical safety of the whole FLNG plant. The present study focuses on developing a novel methodology to design an inherently and optimally safer layout for the generic multi-deck liquefaction process of an FLNG plant. The integrated inherent safety principle is applied at the early phases of the layout design considering inherent safety and cost indices in three different layout options, and for the final design the most optimal option was selected. The proven indexing approach quantified the associated risks in all units. Safety measures were undertaken to eliminate or reduce the risk to an acceptable level. The results showed that the economic losses due to domino effects were limited by an improved layout design and passive control strategies. This study only dealt with evaluation and analysis of critical units of the plant due to a lack of detailed information at the early phase of the design. However, the proposed method plays a positive role in obtaining an inherently safer layout design of any multi-deck plants.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".