Adoption Of Ceramic Membrane Technology In Albertas SAGD Operations
Bibliographic record
Abstract
Oil sands operations are seen to have a negative impact to the environment by producing a large quantity of green house gas emissions as well as being a large consumer of water during the extraction process. As of right now, natural gas prices are too low to consider using carbon capture storage technologies to generate heat and electricity in SAGD operations which has led to the government of Alberta missing its target of reducing their green house gas emissions (CBC, 2013). The alternative is to improve the existing methods and technologies that decrease the water consumption as well as green house gas emissions. COSIA, in partnership with numerous oil and gas companies, are currently working on using ceramic membranes in their SAGD operations. What this new technology means is that it reduces the overall environmental impact by decreasing their total overall water consumption, improving efficiencies, energy expended on steam generation, and reducing their green house gas emissions, while still being able to use the same methods for oil extraction in Alberta’s oil sands. Ceramic membrane technology would allow the water recycling process to remove a number of steps as well as reducing the cost of de-oiling systems, filters, and lime softening (COSIA, 2013) However one of the main barriers that needs to be overcome is the cost to implement this technology means stopping production at a SAGD facility for a period of time which could effect the price of oil (COSIA, 2013).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".