Shell Cansolv CO2 capture technology: Achievement from First Commercial Plant
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The first commercial post combustion CO 2 capture plant, based on regenerable amine technology, designed by Shell Cansolv was started successfully in Q3, 2013. Subsequently, the plant conceded 72 hrs warranty test run and since then, the CO 2 capture plant has been running smoothly. The plant performance met requirements for a successful warranty test run and in most cases results were considerably better than expected. The CO 2 capture facility is designed to capture 170 tonnes of CO 2 /day from a gas-fired boiler's emissions, however, due to boiler limitations, the plant is running at a capacity of 120 tonnes of CO 2 /day. The capture facility is equipped with a prescrubber followed by an absorber and water wash on the capture side. CO 2 was captured using counter current exchange with the Cansolv DC-103 solvent, which was regenerated in the stripper. At normal operation, the average CO 2 capture was maintained at around 90%, however, CO 2 capture as high as 98% is achieved. The average specific steam consumption over the Performance Test period was noticed as <1.05 kg of steam/per kg of CO 2 captured. The average CO 2 product purity was greater than 99.0%, more specifically as high as 99.8%. The capture unit is equipped with a semi-batch thermal reclaimer (TR) unit to remove ionic and non-ionic degradation products from DC-103 solvent. The average amine recovery from thermal reclaimer unit was 99.75% with a maximum of 99.8%. The CO 2 capture performance, energy consumption, Amine recovery from Thermal Reclaimer, and operational philosophy exercised at the CO 2 capture plant will help Shell Cansolv to optimize the design of the CO 2 Capture unit further. Learning's from first commercial CO 2 capture plant will help Shell Cansolv to successful start-up of upcoming commercial projects.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it