Advancements of Shell Cansolv in Post-Combustion CO2 Capture Technology
Bibliographic record
Abstract
Abstract An affordable and reliable Carbon Capture&Storage (CCS) technology is the key step to reduce CO2 emission from new and existing coal-fired and Gas power plant, as well as large industrial sources. With the milestone start-up of the Boundary Dam Carbon Capture and EOR project (Saskatchewan Canada, 2014), Shell Cansolv's technology became the world's first technology deployed in post-combustion carbon capture at commercial scale in the coal-fired power industry. This coupled with on-going operations in post-combustion CO2 capture for utilization in the industrial and chemical market, as well as an on-going front end engineering design (FEED) for CO2 capture from a combined cycle gas turbine (CCGT) power station. The Shell Cansolv CO2 capture technology has accumulated a spectrum of experience and learnings covering Enhanced Oil Recovery (EOR), CCU and CCS. To open, this presentation will provide a project summary of Shell Cansolv in an on-going EOR, CCU and CCS application. Moreover, it will focus on the Shell Cansolv CO2 capture technology, providing a detailed technical description of how the line-up is adapted per application and provide rationale and relative performance indicators in each. A summary of the balance required to deliver the best Net Present Value (NPV) solution will be provided, such as the important considerations of optimization trade-offs on a per project basis, including: Increasing NPVOperations simplicityAcceptable Scale-up instead of multiple trainsEvolution in CANSOLV new solvents, their specifications and applicationsIntegration complexity vs. stand-alone robustnessA design for maximized availability, flexibility and cost-effective In conclusion, a description of some of the key learnings accumulated in each application will be discussed, as well as an indication of where we think the next breakthrough changes can and will be made.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".