Bibliographic record
Abstract
Abstract Industrial facilities typically have long lives and consume large amounts of energy year by year during their operations phase. Intervention in the design stage to independently review energy efficiency and the use of renewable energy inherent in the original design can identify opportunities that will offer significant benefits on a life cycle basis. The energy design review concept can be applied to any project for expansion or upgrade of an existing industrial plant or to Greenfield industrial projects. The approach is to carry out an independent cross functional review of the design for the new industrial facilities to identify opportunities to use energy more efficiently and/or use more renewable energy. These reviews should be carried out with the project team to identify opportunities that have reasonable incremental capital costs and have little or no impacts on safety, maintenance, reliability and operating flexibility of the future facility. On completion of the review, the prioritized opportunities would then be assessed as part of the corporate approval process for the design of the project and those ideas that are accepted would then be carried forward by the project team. In a recent energy design review assignment for a new oil sands processing plant, Hatch identified a number of opportunities which taken together could reduce the future facility's annual energy use by up to 40%. Energy design reviews can have a significant impact on industrial energy use and are applicable at each stage of project development from the conceptual level through to project implementation.
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 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 | 0.014 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.073 | 0.032 |
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".