Design and Operation of a Sulfur Supply Chain for Sour Gas Processing and Bitumen Upgrading Operations
Bibliographic record
Abstract
Extensive research has been done on the components that constitute the sulfur supply chain, including sulfur recovery, storage, forming, and distribution. The research focus was on improving the efficiency and environmental sustainability of each of these areas rather than focusing on the supply chain as a whole. The aim of this work is to design a sulfur supply chain that integrates these components within a single framework. It represents a starting point in understanding the trade-offs involved in the sulfur supply chain from an optimization point of view. Optimization and mathematical modeling techniques were implemented to generate a decision support system that will provide an indication of the optimal design and configuration of sulfur supply chains. The resulting single-period mixed-integer linear programming (MILP) model was aimed at minimizing total infrastructural and operational costs. The model was illustrated through a case study based on Alberta’s Industrial Heartland (AIH). A deterministic approach in an uncertain environment was implemented to investigate the effect of supply and demand variability on the design of the supply chain. This was applied to two scenarios, which are steady state operation and sulfur surplus accumulation. The results for the investigated case study reveal that the optimum sulfur supply chain might consist of medium-to-large sulfur forming facilities serving multiple producers. The model also identified the locations of forming facilities, the forming, storage and transportation technologies, and their capacities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".