Demonstration Results of Enzyme-Accelerated CO2 Capture
Bibliographic record
Abstract
CO 2 Solutions Inc. of Québec, Canada has commercialized a proprietary low-cost, enzyme accelerated solvent technology for the capture of carbon dioxide and its beneficial reuse. Enzyme catalyzed aqueous salt solutions can be deployed with a variety of gas scrubbing equipment configurations to replace costly and environmentally challenged amines solvents as an efficient solution for post-combustion CO 2 capture. The presentation will discuss the results of its 10 tonne-CO 2 /day demonstration carried out in the summer and fall of 2015 in Salaberry-de-Valleyfield (“Valleyfield), near Montreal, Canada. The Valleyfield project was the largest-ever scale test of an enzyme-based CO 2 capture process and used an industrially robust form of carbonic anhydrase enzyme developed by CO 2 Solutions to capture CO 2 from a natural gas fired boiler. The demonstration operated successfully for a total of more than 2,500 hours and saw stable performance of the enzyme catalyst, stable solvent performance with negligible degradation, no wastes generated, and highly pure CO 2 produced suitable for a broad range of reuse applications. In addition, the use of low-grade, nil-value heat for solvent regeneration at low temperature was accomplished, demonstrating this innovative method of operating cost savings. The presentation will include performance aspects of the demonstration and associated techno-economics of the technology for application to electricity and steam generation. An update on the technology's commercial implementation will also be provided.
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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".