Valve Placement and Operation for Liquid Transmission Pipelines: A Risk Reduction Tool
Bibliographic record
Abstract
The Intelligent Valve Placement (IVP) approach has been developed to incorporate risk (defined as likelihood × consequence) reduction techniques to identify optimum locations for sectionalizing (block) valves along existing or new petroleum transmission pipelines. This process aims to optimize valve placement based on risk reduction rather than current rule-of-thumbs and regulatory requirements. It may result in cost reduction and cost optimization through efficient valve placement while meeting required regulations. This approach incorporates risk through reduction of consequence, which is achieved by reducing the potential spill volumes and impact to sensitive areas in an iterative manner. With the use of consequence reduction strategies, this paper demonstrates that valve placement and operability decisions on both new or existing pipelines can be made to optimize the location of valves and provide for a safer pipeline. Although this process is highly consequence driven, there are opportunities to incorporate likelihood drivers. Equations have been developed that quantify consequence reduction and allow for determination of an optimal valve placement design. Application of this technique to pipelines in the United States is presented and the limitations of this method addressed. Although this technique is valid anywhere, this approach is in line with the requirement from the U.S. Department of Transportation to examine the placement of emergency flow restricting devices as per the High Consequence Area ruling and the requirements set forth by the Canadian Standards Association regarding remote operation of valves in Canada consistent with “extraordinary hazard” determinations.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".