Cenovus 10 MW CLC Field Pilot
Bibliographic record
Abstract
It requires energy to extract bitumen from the vast Alberta oil sands resources, which results in greenhouse gas (GHG) emissions. Even though natural gas, the least carbon intensive fossil fuel is used to produce steam for bitumen recovery from in situ reserves, emissions of GHG continue to increase as the bitumen output is increasing annually. There is an urgent need to develop alternative lower CO2 avoidance-cost carbon capture technologies, to mitigate these emissions. Chemical looping combustion (CLC) is an inherently CO2 capture ready steam generation technology and has the potential of lower CO2 avoidance cost. Cenovus Energy Inc. (Cenovus) engaged ANDRITZ Energy & Environment GmbH (AE&E) and Vienna University of Technology (TUV) to complete a preliminary design of a 10 MW CLC steam generator pilot (CLSG). It is designed to produce 16.5 tonnes per hour of 100 bar 100% quality steam using natural gas. Cenovus plans to install and operate it in its Christina Lake Thermal Project (Host). The Pilot will be completely integrated with the Host who will use the steam for oil production using Cenovus’ steam assisted gravity drainage (SAGD) process. The successful demonstration of this Pilot will pave the way for design, construction and operation of commercial CLC boilers by 2020. This paper will discuss the CLSG designs, its development status, the test program to validate the performances of the 10 MW CLSG and the first generation NiO oxygen carrier.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".