Energy consumption benchmark guide : Conventional petroleum refining in Canada
Bibliographic record
Abstract
This document was developed to provide a tool for use when comparing a petroleum refinery with another, as well as allowing managers to make decisions concerning energy use and efficiency. The three major objectives of this guide were: the provision of a summary picture of the petroleum refining industry from the perspective of energy consumption and production, the provision of an indication into the variation of efficiencies and intensities existing within the industry, the provision of a benchmark comparison to be used between one plant and the next, and the provision of some indication concerning the relative road to action with regard to energy intensity and efficiency. Of the 21 petroleum refineries in Canada, 7 are located in Ontario and 5 in Alberta, with three each in British Columbia, Quebec and the Atlantic provinces, and the last one is located in Saskatchewan. Brief background information on the industry was provided, followed by a historical energy use profile. The fuel use trends were discussed, and the next section examined benchmarking. How to benchmark your plant was described and achievements reviewed. The achievements included the installation of a heat recovery system on a crude unit at one of Petro-Canada's refineries to recover waste energy and reuse fuel in feed furnaces; process upgrades initiated by Shell Canada Limited at its facilities to improve efficiency of steam and hot water systems, vacuum pumps, furnaces and compressors; and a Global Energy Management System (G-EMS) implemented by Imperial Oil Limited at the Strathcona refinery. 7 figs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".