High Pressure Gas Pipelines: Trends for the New Millennium
Bibliographic record
Abstract
The end of the 20th century has seen some major developments to the business of pipelines worldwide. In North America and Europe the trend has been toward deregulation of the industry. In other markets the trend has been toward the use of fixed transport cost contracts between shippers and the pipeline company. The net effect of these changes is increased competition in the transport of energy with the resulting requirement to provide the lowest cost of transport. At the same time pipelines need to maintain the traditionally high levels of safety and reliability that customers, the public and regulators have been accustomed to. The pipeline industry has responded to the challenge to reduce costs on a number of fronts. These include the areas of contracting, financing, planning, regulation, market development, and technical developments as well as many other areas. This paper will focus on technical developments that have allowed pipeline companies to reduce the cost of moving large volumes of natural gas at high pressures. Progress that the industry has made in the areas of capital cost reduction will be illustrated by an example of high pressure pipeline design. Capital costs will be compared for five system design pressures that all result in the same maximum flow rate. The optimum high-grade steel will be chosen for each pressure. This will also be compared to costs for using Composite Reinforced Line Pipe (CRLP) a new technology for the pipeline industry.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".