Supply chain optimization of flare‐gas‐to‐butanol processes in alberta
Bibliographic record
Abstract
ABSTRACT In this work, the economic feasibility of combining a novel portable gas‐to‐methanol process with a novel methanol‐to‐butanol process is examined. The gas‐to‐methanol process converts waste flare gas into methanol using a series of truck‐mounted devices deployed at oil production wellheads. The methanol‐to‐butanol process uses a new proprietary catalyst which produces butanol via a diketene intermediate at a large centralized facility. The goal of this work is to identify the best ways of commercializing this technology in Alberta. To do this, a supply chain optimization model is formulated which considers specifically how many gas‐to‐methanol trucks should be used and where specifically in Alberta they should be deployed, the specific suppliers of CO 2 to use, where the location of the central methanol‐to‐butanol facility should be chosen, and the costs of transportation of materials between locations. The model framework also considers the possibility of getting methanol in full or in part by alternative means such as producing methanol from conventional pipeline natural gas, or purchasing methanol from petrochemical or biomass‐based routes. The supply chain optimization problem is formulated as a nonconvex NLP and BARON is used in a Pareto analysis considering weighted combinations of economic and environmental objective functions. The resulting analysis provides a variety of possible viable strategies which can provide both profitability and reduced environmental emissions in Alberta by using a combination of the novel portable flare gas capture devices with more conventional gas‐to‐liquids technologies.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".