Active control of natural gas pipeline system based on Box-Jenkins method
Bibliographic record
Abstract
As the integrated energy network is gradually established, uncertainties in the upstream and downstream of the natural gas system have significantly increased. This places higher demands on the intelligence of natural gas pipeline system, which must be capable of independently analyzing market fluctuations and proactively adjusting operational plans. However, due to the non-convexity and strong nonlinearity of the partial differential equations (PDEs) that control transient natural gas flows, and traditional methods are inadequate for meeting the fast control requirements of modern intelligent regulation. In this paper, the active control method for NGPS, including state inversion control and linear quadratic regulator (LQR) control algorithm, is established based on the Box-Jenkins approach to achieve rapid control under transient conditions. Severals case studies on a natural gas pipeline system demonstrate the effectiveness of the proposed active control method. It is shown that transient operation schemes for the compressor can be formulated under fluctuating outlet flow conditions to make the delivery pressure controlled near the contractual pressure. The control error is less than 0.5%, fully meeting the actual field requirements, and compared to constant-pressure delivery, it reduces energy consumption by 8,342 kWh per day. The proposed active control method strictly adheres to the characteristic equations of transient natural gas flow, avoiding the need to solve large-scale optimization problems. Additionally, it contributes to energy-saving and emission reduction objectives in natural gas pipeline operations, providing technical support for the intelligent regulation of future natural gas pipeline system.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".