General optimization model for the energy planning of industries including renewable energy: A case study on oil sands
Why this work is in the frame
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Bibliographic record
Abstract
A multi‐period optimization model is developed for the energy procurement planning of industries including renewable energy. The model is developed with the objective of identifying the optimal set of energy supply technologies to satisfy a set of demands (e.g., power, heat, hydrogen, etc.) and emission targets at minimum cost. Time dependent parameters are incorporated in the model formulation, including demands, fuel prices, emission targets, carbon tax, lead time, etc. The model is applied to a case study based on the oil sands operations over the planning period 2015–2050. Various production alternatives were incorporated, including renewable, nuclear, conventional and gasification of alternative fuels. The results obtained indicated that the energy optimization model is a practical tool that can be utilized for identifying the key parameters that affect the operations of energy‐intensive industrial operations, and can further assist in the planning and scheduling of the energy for these industries. © 2016 American Institute of Chemical Engineers AIChE J , 63: 610–638, 2017
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it