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Enregistrement W4385887943 · doi:10.55274/r0010956

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

2009· report· en· W4385887943 sur OpenAlexaboutno aff
Wilcox

Notice bibliographique

Revuenon disponible
Typereport
Langueen
DomaineEngineering
ThématiqueOffshore Engineering and Technologies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésEngineeringHazardous wasteCompressor stationWork (physics)Forensic engineeringAeronauticsGas compressorTransport engineeringMechanical engineeringWaste management

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,841
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,011
Tête enseignante GPT0,236
Écart entre enseignants0,225 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2009
Routes d'admission1
Résumé présentoui

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