MétaCan
Menu
Back to cohort
Record W19455574 · doi:10.5006/c2001-01401

Huge Capacity Thermoplastic-Lined FRP Chemical Storage Tanks

2001· article· en· W19455574 on OpenAlexaffabout
Greg Landry

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSpacecraft and Cryogenic Technologies
Canadian institutionsMaple Leaf Foods
Fundersnot available
KeywordsFibre-reinforced plasticStorage tankMaterials scienceComposite materialThermoplasticWater tanksWaste managementEngineering

Abstract

fetched live from OpenAlex

Abstract Thermoplastic-Lined, Fiberglass Reinforced Plastic (Dual-Laminate) tanks and vessels are typically employed in service conditions that are beyond the limits of even the high performance resins, used in FRP fabrication. Therefore, their design and manufacture requires a great deal of expertise in both the processing and fabrication of the thermoplastic liner, as well as in the design of the FRP structural laminate. A significant investment in tooling and manufacturing equipment is also necessary, to ensure that a high level of quality is achieved in the thermoplastic liner fabrication. Thermoplastic-Lined FRP equipment is generally produced under the controlled conditions of the manufacturer’s plant, which limits the dimensions of the equipment to whatever can be transported to its final destination. When a project in the Far East called for twenty-eight vessels and towers, some of which were close to thirty feet in diameter, Fabricated Plastics Limited, of Maple (Toronto), Ontario Canada, was awarded the contract and faced several critical challenges in order to satisfy the requirement. This Paper outlines the manufacturing procedures employed to fabricate the Dual-Laminate Tanks and Processing Vessels, as well as detailing the challenges that were faced throughout the manufacturing and shipping of the equipment and how these were addressed.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score0.823

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)0.0010.000

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 head0.012
GPT teacher head0.182
Teacher spread0.170 · 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 designBench or experimental
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
Published2001
Admission routes2
Has abstractyes

Explore more

Same topicSpacecraft and Cryogenic TechnologiesFrench-language works237,207