Hardisty Cavern Storage Facility: Maximizing Process Equipment Utilization Through Innovative Automation Techniques
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".