CO2 Capture and Development of an Advanced Pilot-Scale Cryogenic Separation and Compression Unit
Bibliographic record
Abstract
At present, the use of fossil fuels in the current energy mix represents the largest source of carbon dioxide (CO2) emissions, an important greenhouse gas (GHG), which is largely blamed for global warming. It is estimated that roughly 26 to 30 percent of all CO2 emissions due to human activities come from fossil fuels used for generating electricity. Moreover, a variety of other industrial processes such as oil refineries, fertilizer and cement plants also emit large amounts of CO2. The opportunity therefore exists for a significant reduction of CO2 from industrial processes and power plants through CO2 Capture and Storage (CCS). Currently, there are three main pathways to capturing CO2 from fossil fuel energy conversion processes, namely, pre-combustion capture, post-combustion capture, and oxy-fuel combustion with CO2 capture. Among these approaches, pre-combustion and oxy-fuel combustion take advantage of the fact that CO2 capture is further facilitated by increasing the concentration of CO2 in the flue gas stream, or by increasing the flue gas pressure, or both. There are several different processes available for CO2 capture and compression from low-pressure flue gas streams rich in CO2. These processes vary from simple straight or once through low-temperature separation and compression to more complex processes involving some form of recycle and/or auto-refrigeration. Given the economic constraints often placed on the cost of CO2 capture, and based on energy demand of each process, the ultimate success of these processes hinges on further refining the existing ones or developing new processes that can lower the cost of CO2 capture. The CANMET Energy Technology Centre in Ottawa is currently pursuing a leading research and development program in the field of near-zero emission fossil fuel technologies. This program includes the development of next generation oxy-fuel combustion technologies, as well as the design and development of efficient CO2 capture and compression processes to recover CO2 from oxy-fuel and other fossil fuel energy conversion systems. In this paper, we present and discuss the technical challenges, development stages and commissioning of the CANMET’s pilot-scale CO2 capture and compression unit (CO2CCU). This pilot-scale CO2 separation and compression unit provides an excellent test platform to study the impact of flue gas impurities on the CO2 capture process. This advanced gas separation system is first-of-a-kind pilot-scale unit that represents an integrated approach to oxy-fuel combustion of coal and other fossil fuels with CO2 capture for storage.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".