An Integrated Outflow-Spill Modeling Approach for Risk-Based Valve Placement of Liquid Transmission Pipelines
Bibliographic record
Abstract
Weir et al’s work [1] applied the relative effectiveness of outflow reduction to address the Intelligent Block Valve Placement (IVP) for liquid transmission pipelines. In their work, the effectiveness measure for each potential valve placement location is a length-weighted sum of the calculated volume reduction at all points along the pipeline in which the outflow volumes are multiplied by weighting factors that reflect the relative importance of spills in different sensitive areas; In our work, the original approach was enhanced to more quantitatively reflect both the likelihood of line failure and the full consequences of line failure as impacted by additional valve placement considering both block and check valves. This paper presents an IVP approach integrated with a quantitative risk assessment through which block and/or check valve placement schemes are optimized. The process involves a computer analysis in which block and check valves are iteratively selected and placed for each case. The risk reduction associated with each case is determined as the product of failure likelihood and the weighted average cost reduction. Failure likelihood is typically quantified using reliability methods or industry failure statistics, and is not the focus of this paper. The cost reduction focuses on environmental factors, which are represented by the clean-up cost of a spill that impacts both sensitive and non-sensitive areas for each incidence. In modeling consequence, the reduction of outflow potential is quantified by an in-house outflow simulation tool; and the potential spill impact is assessed through a mechanistic in-house VBA extension of ArcGIS, a three-dimensional (3-D) overland-hydrographical spill simulation package. Optimal valve placement design is achieved by balancing the costs associated with environmental risk with the costs associated with installing and maintaining block and check valves. The valves included in the assessment for outflow simulation and cost analysis are check valves and block valves. The automatic valve placement simulation is terminated when the valve installation/maintenance cost outweighs the benefits of placing more valves in the line.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".