An Optimal Flow Allocation Model of the Natural Gas Pipeline Network Considering User Characteristics
Bibliographic record
Abstract
The fundamental function of a natural gas pipeline network is to transport enough natural gas to users. Therefore, user characteristics should be considered in the formulation of the flow allocation plan of the pipeline network under accident conditions. However, user characteristics have usually not been considered in previous flow allocation models. In this study, a mixed integer linear programming model is developed to determine the optimal flow allocation plan of a large-scale and complex natural gas pipeline network under accident conditions, and the user characteristics are considered as well. The optimization objective is to maximize the weighted sum of the amount of natural gas transported to the consumers under accident conditions, and the weights of the natural gas users are obtained by user characteristics analysis. The model constraints include flow constraints, gas source supply capacity constraints, user demand constraints, pipeline transmission capacity constraints, pressure constraints, and pipeline hydraulic constraint. For the sake of model simplification, the hydraulic constraints are treated piecewise linearly. Furthermore, the model is set into a real-world situation, which is the natural gas pipeline network located in China, and the user characteristics are considered in the optimal flow allocation plan under accident condition. The impact of user characteristics is further investigated by calculating and comparing the flow allocation plan when considering and ignoring user characteristics. The study indicates that when user characteristics are considered, the natural gas pipeline network will tend to give higher priority to those crucial users.
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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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".