Transient-State Natural Gas Transmission in Gunbarrel Pipeline Networks
Bibliographic record
Abstract
We study the energy consumption minimization problems of natural gas transmission in gunbarrel structured networks. In particular, we consider the transient-state dynamics of natural gas and the compressor’s nonlinear working domain and min-up-and-down constraints. We formulate the problem as a two-level dynamic program (DP), where the upper-level DP problem models each compressor station as a decision stage and each station’s optimization problem is further formulated as a lower-level DP by setting each time period as a stage. The upper-level DP faces the curse of high dimensionality. We propose an approximate dynamic programming (ADP) approach for the upper-level DP using appropriate basis functions and an exact approach for the lower-level DP by exploiting the structure of the problem. We validate the superior performance of the proposed ADP approach on both synthetic and real networks compared with the benchmark simulated annealing (SA) heuristic and the commonly used myopic policy and steady-state policy. On the synthetic networks (SNs), the ADP reduces the energy consumption by 5.8%–6.7% from the SA and 12% from the myopic policy. On the test gunbarrel network with 21 compressor stations and 28 pipes calibrated from China National Petroleum Corporation, the ADP saves 4.8%–5.1% (with an average of 5.0%) energy consumption compared with the SA and the currently deployed steady-state policy, which translates to cost savings of millions of dollars a year. Moreover, the proposed ADP algorithm requires 18.4%–61.0% less computation time than the SA. The advantages in both solution quality and computation time strongly support the proposed ADP algorithm in practice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".