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Record W1996007824 · doi:10.1115/ipc2004-0678

Hardisty Cavern Storage Facility: Maximizing Process Equipment Utilization Through Innovative Automation Techniques

2004· article· en· W1996007824 on OpenAlexaboutno aff
Melvin Neufeld, Bruce A. Miller

Bibliographic record

Venue2004 International Pipeline Conference, Volumes 1, 2, and 3 · 2004
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsAutomationFlexibility (engineering)EngineeringProcess (computing)OperabilityHorsepowerAutomotive engineeringReliability engineeringComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

The Hardisty Cavern Facility at Hardisty, Alberta — consists of four underground salt caverns totalling 3.0 million barrels of petroleum products storage — was recently completed. This project is unique in that it integrates existing underground salt caverns into a significant North American crude oil transportation hub. Approximately 400 million barrels of oil move through this hub annually. This project utilizes existing caverns developed in the late 1960’s with significant upgrades and new infrastructure to integrate the Hardisty Cavern Facility into the crude oil transportation hub. This paper discusses the automation related innovations implemented and the challenges encountered during the course of the project. One example of innovation involves utilizing a single variable frequency drive (VFD) to perform multiple functions. Due to process requirements, the VFD was required to operate one or two cavern injection pumps. Electrical power grid constraints dictated that the VFD be used for starting and stopping the 1500 horsepower (1119 kW) pump motors. Process conditions also required that the pump motor loads be automatically transferred from VFD to the utility power grid without interruption to production. Operational flexibility was another key component of the facility automation requirements. Storage requirements for multiple petroleum products necessitated operator-selectable flow paths within the automation system. In addition, flexibility, safety, efficiency and maintainability requirements resulted in a distributed process philosophy across three separate process areas.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.935
Threshold uncertainty score0.937

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.029
GPT teacher head0.277
Teacher spread0.249 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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
Published2004
Admission routes1
Has abstractyes

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