Eliminating Cost and Space Barriers for Small-To-Midsize Emitters Through Fully Columnless and Modular Carbon Capture Technology
Bibliographic record
Abstract
Abstract Scope This paper presents Carbon Clean's CycloneCC™ technology, a fully modular and columnless post-combustion carbon capture solution designed to address the barriers facing heavy industry. By reducing capital expenditure and footprint, CycloneCC makes carbon capture more economically viable. The technology combines rotating packed beds (RPBs) and Carbon Clean's proprietary APBS-CDRMax®solvent. The goal is to demonstrate the use of first-of-a-kind (FOAK) technology in carbon capture and offer a cost-effective pathway to decarbonizing the hard-to-abate sector. Methods Carbon Clean has demonstrated the concept of modular intensified post-combustion carbon capture through pilot-scale and factory testing of RPBs and advanced solvent technology. CycloneCC combines two proven process intensification technologies: APBS-CDRMax, a proprietary amine-promoted buffer salt solvent. Rotating packed beds (RPBs), which utilise centrifugal force to enhance mass transfer. The RPBs contain a disk of packing material that rotates around its axis. The centrifugal force generated through this rotational motion is significantly greater than the gravitational force seen in conventional columns. APBS-CDRMax has been developed by Carbon Clean and tested at the world's largest CO2 capture demonstration facilities. Conclusion CycloneCC is currently the most developed modular carbon capture solution for industrial applications, enabling a 30% reduction in CapEx and OpEx. Compared to traditional solutions using 30% wt% MEA, APBS-CDRMax delivers a 20-fold improvement in corrosion resistance, 10-fold improvement in solvent degradation, and 30% lower energy consumption. The process intensification of the technology has reduced footprint by 50%, height by 70% and steel usage by 35%. An industrial-scale demonstration with a leading energy company was conducted at the Al Ruawais Industrial Complex in Abu Dhabi. The unit was installed within a week due to the prefabricated design, which reduced operational disruption and safety risks. The unit operated for around 4,000 operating hours over a six-month period, capturing CO2 from a reformer flue gas stack and utilizing it in urea production. CycloneCC is now at Technology Readiness Level (TRL) 7 and has been deployed at a site in Saudi Arabia and Canada with key partners for further validation in varied climates and at different CO2 concentrations in the inlet flue gas. The use of AI has allowed reliable adjustments, with human operators implementing AI-suggested recommendations to increase reliability and availability. Recent factory testing confirmed CycloneCC C1 RPBs’ mechanical performance, including the ability to capture 285 tonnes of CO2 per day, representing a 20-fold RPB scale-up from the first industrial demonstration of CycloneCC. Additive Information Carbon Clean will provide FEED support to maritime decarbonization with an engineering partner. The study envisages the installation of a CycloneCC unit on an FPSO vessel as a pilot, and it will be a first-of-a-kind deployment for onboard carbon capture. CycloneCC is well-suited to an offshore environment, as the unit delivers a height reduction of 70% when compared to conventional solutions. The RPBs will achieve enhanced capture performance under vessel motions, making CycloneCC ideal for offshore operations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".