Optimal Infrastructure of the Upgrading Operations in the Oil Sands under Uncertainty: A Multiscenario MINLP Approach
Bibliographic record
Abstract
The upgrading operations in the Oil Sands are a key asset to Canada’s economy since they currently process more than 60% of the crude bitumen extracted from this region. Despite their significance and relevance, mathematical tools that can evaluate the infrastructure and economy of the upgraders’ operations under fluctuations and variability in key operational and economic factors are limited. It is the purpose of this study to present a stochastic optimization model that has been developed to specify the optimal infrastructure that may be required by the Oil Sands’ upgraders to satisfy the projected production demands at minimum cost in the presence of uncertainty in key economic and operational parameters. A multiscenario approach, describing the potential realizations in the uncertain parameters affecting the upgrading operations, is employed in this work to identify the upgraders’ infrastructure that can simultaneously accommodate the projected production demands and the uncertainty in the system’s parameters. A case study featuring the upgrading operations for year 2035 under uncertainty in key economic and operational factors is presented. The results show significant variability in the upgraders’ infrastructure, and their corresponding energy costs, when this system is evaluated under different uncertain scenarios.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".