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Record W4312246028 · doi:10.1115/ipc2022-86889

Explained: Ultrasonic Self-Propelled Robotic Inspection Solution for Unpiggable Dock Line

2022· article· en· W4312246028 on OpenAlexaboutno aff
Corey J. Richards, Cedric Bolduc, Børge Hamnes

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMarine and Offshore Engineering Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDOCKEngineeringScheduleDowntimePiggingRefineryTerminal (telecommunication)Pipeline transportComputer scienceMarine engineeringTelecommunicationsReliability engineeringMechanical engineeringWaste management

Abstract

fetched live from OpenAlex

Abstract While under various banners, Valero Energy Inc. has been a staple in the refining and marketing of products since 1961. Key facilities such as the Montreal East Terminal and the St-Romuald Refinery supply their carriers and customers (resellers and wholesalers) in the regions of Southern Quebec, the Maritimes, and the Greater Ottawa and Toronto areas, with smaller terminals in these areas serving the Northeastern United States market. Guaranteeing that delivery orders are met and critical product is delivered on schedule requires Valero’s terminals to have comprehensive integrity programs in place. Comprehensive programs, which include risk modeling, in-line inspections and tank inspections, to name a few, help to ensure there is no unexpected downtime due to incidents. While ensuring the integrity of any oil and gas pipeline network contains its own challenges, facility or terminal piping does not often contain the typical infrastructure to employ standard technologies. Accordingly, specialty solutions are often required. Such is the case for Valero’s 960-meter, 10inch dock line at the Gaspé Terminal, which is a critical supply link transporting refined products from shipping vessels to the terminal. The technical challenges that needed to be overcome for the inspection of this underground dock line included: • No pig launcher and pig receiver present; dock line not designed for conventional pigging • Dock line only has flow when unloading to ships • In-service operating conditions prohibited conventional ILI tool inspection • Limited space available for equipment to support standard pigging operation • Staging of equipment on the dock would pose additional risks for the environment in the event of loss of containment for any supporting pumping equipment This paper will outline the validation, testing and execution of a self-propelled robotic tethered solution in combination with an ultrasonic wall measurement (UTWM) system capable of navigating the complexities of the Gaspé dock line. The inspection solution outlined in the paper did not require any major modifications to the dock line system, nor did any equipment need to be present on the dock side. The utilization of dedicated crawler units eliminated the requirements for flow or pressure in the dock line to propel the tool during inspection. Overall technical and operations benefits of this solution are: • Accurate and precise feature classification and sizing by quantitative ultrasonic measurement • Two sets of data (from both the inbound and outbound run) with the outbound run providing a further verification for the inbound run • Real-time data analysis and preliminary report on-site, followed by a thoroughly reviewed final report The online self-propelled tethered crawler inspection system contained the actual inspection tool, the drive unit, the umbilical winch and a computer system to communicate with and control the inspection vehicle. The paper will outline in detail the workings of the inspection tool, particularly: • The ridged ring UT sensor unit • The modifications and testing to ensure the system could pass features in the line • The electrically driven propulsion system While the focus of this paper is the deployment of this inspection solution in the Gaspé dock line, it will also compare previous operational experiences with running a free-swimming tool. Overall, the paper will outline not only how this solution better ensured the integrity of the line itself, but also how its execution reduced safety and environmental risks, while still collecting indispensable, high-quality in-line inspection (ILI) data.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.911
Threshold uncertainty score0.644

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 communication0.0000.000
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 head0.010
GPT teacher head0.196
Teacher spread0.186 · 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 designSimulation or modeling
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
Published2022
Admission routes1
Has abstractyes

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