Scale-Up and Transient Operation of CO2 Capture Plants at CO2 Technology Centre Mongstad
Bibliographic record
Abstract
Abstract The CO2 Technology Centre Mongstad (TCM) is the world's largest facility for testing and improving technologies for post-combustion CO2 capture. The knowledge gained will prepare the ground for full scale CO2 capture initiatives to combat climate change. TCM is a joint venture between the Gassnova, Statoil, Shell and Sasol. It is located at the West coast of Norway, north of the city Bergen. This paper will discuss the scale-up and transient operation of amine based post-combustion CO2 capture plants in general, and presents some typical results. Scale-up and transient operation are typically among the last topics to be assessed in the technology development process because it requires bigger plants. Results were used from the monoethanolamine (MEA) campaign that was executed in fall/winter 2013/2014. The discussion on scale-up showed that emissions to air, material choice, construction method, vapour/liquid distribution and reclaiming are important technical aspects. Normalized transient data were presented for 7 important variables during a controlled plant stop-and-restart and a sudden stop-fast restart case. Stable CO2 product flow could be obtained after 3–4 hours, while stable emissions and CO2 product temperature took 1–2 hours more. NH3 emissions showed a peak after restart due to accumulation in the solvent during the stop. It was concluded that amine based CO2 capture plants should be able to follow their power plants without significant additional CO2 emissions.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".