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Record W4385887943 · doi:10.55274/r0010956

PR-015-09200-R01A Compressor and Pump Station Incidents and Technology Gaps

2009· report· en· W4385887943 on OpenAlexaboutno aff
Wilcox

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsEngineeringHazardous wasteCompressor stationWork (physics)Forensic engineeringAeronauticsGas compressorTransport engineeringMechanical engineeringWaste management

Abstract

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In 2008, Pipeline Research Council International (PRCI) took the initiative to identify the main causes of reportable incidents in compressor and pump stations. Data was gathered from several sources including the United States� Department of Transportation Pipeline and Hazardous Materials Safety Administration, Canada�s National Energy Board, and PRCI member companies. More than 1600 incidents were reviewed over an 18 year period (1990 to 2008). The incidents were evaluated based on their frequency of occurrence and the consequences of the incidents (injury, ignition, environmental impact, etc�). In pump stations, pump seals, valves out of sequence due to operator error, and gasket and bolting were identified as the highest impact incidents types. In compressor stations, the three highest impact incident types were found to be pipe components, natural forces (hurricanes and lightning strikes), and gaskets and bolting. During the 2008 project, research roadmaps were developed based on the results of the incident data review. In the process of defining the research projects, a brief review into the available technology for the incidents types was conducted. It was quickly found that a more detailed state-of-the-art review was needed to accurately identify the research required for several of the incident areas. Therefore, a state-of-the-art review of the three highest impact incidents in pump and compressor stations was proposed. The work documented in this paper is the state-of-the-art review of these incidents. In the PRCI CPS 9-1 (2008) project, it was found that more information was needed on several of the incidents in order to fully define the root cause. Therefore, the first task of the PRCI CPS 9-1 (2009) effort was to attempt to gather more information on the top three impact incident types. Thirty-two pipeline companies were contacted and additional information was provided for approximately 25% of the incidents. From the review of this additional and past data, several focus areas were identified for the state-of-the-art reviews. The state-of-the-art studies included a survey of the current technology, identification of common failure mechanisms, and review of strategies to reduce incident occurrences. These studies are reviewed in detail in the appendices of this document. From the state-of-the-art studies and the incident review, technology gaps were identified. Technology gaps are areas where new innovative technologies or applications are required to address current inspection/maintenance strategies for a particular piece of equipment or task. Technology gaps were only identified for pump seals. These gaps included the inability for pump seals to survive process upset conditions, inability to correctly identify and model expected loads and operating conditions for pump seal selection, and lack of installed seal inspection or life prediction methods except through leakage detection. All other incident types (valves out of sequence due to operator error, gaskets and bolting, pipe components, and natural forces) have adequate technology to address the incident occurrences. In the majority of the incidents, even though the technologies exist, it may not be used or applied correctly. Several recommendations were made for future work. These included work that a company may consider conducting internally to reduce the occurrence of incidents and future research. The recommendations for future work for operators and research for industry are summarized in a list below. Research items included on the research roadmaps are indicated with an asterisk.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.841
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.236
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

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