ETI Insights Report - Reducing the cost of CCS - Developments in Capture Plant technology
Bibliographic record
Abstract
Successfully deploying Carbon Capture and Storage (CCS) would save tens of billions of pounds to consumers and businesses – providing low carbon electricity, capturing industrial emissions, creating flexible low carbon fuels and delivering negative emissions in combination with bioenergy.CCS is a combination of proven technologies. Injection of CO2 from an ammonia plant for enhanced oil recovery (EOR) began as far back as 1972 and “first intent” CCS began at Sleipner in 1996. By 2017, 22 plants will be running CCS technology applications, spanning post combustion and pre-combustion coal, natural gas steam reforming, bioenergy CCS (corn to ethanol), and applications from power, gas production, refining, chemicals and steel.The potential for cost reduction through deployment is significant:Investment in anchor projects provides transport and storage infrastructure for subsequent projects to build onReductions in scope and increased project sizes to exploit economies of scale.Risk reduction during the early stages of CCS deployment should attract more competitive financing. Developers in the US, UK and Canada have committed to publically sharing their CCS designs and early operational experiences such that future projects can benefit.Additional cost reduction can be achieved through innovation in capture technology:Post combustion capture, based on mature amine gas separation technology, has seized the largest share of the power market and still offers opportunities for further improvements.Pre-combustion gasification technology potentially offers a clean, flexible alternative for coal, biomass and waste, and significant research, development and demonstration (RD&D) funding is resulting in continuous improvements, now feeding into demonstrations.Other promising options using membranes, hydrates, cryogenics, enzymes, fuel cells and carbonate chemistry are actively being developed, and progressing towards commercialisation, such as vacuum swing adsorption (VSA) in a refinery in the USA. Post combustion temperature swing adsorption (TSA) has the potential to compete with amines in the future, but next generation adsorbents are still at a relatively early stage of their development.NET Power’s supercritical CO2 technology has the potential to be a game-changing technology. It faces several new challenges in equipment design and operation, but testing is under way. It is likely to take several years before it can be demonstrated at full scale.Given the current immature status of the next generation of alternatives, amines or pre-combustion are likely to be the most investable options for the next five to ten years.One pathway to reducing the cost of CCS is deploying a small number of full scale plants sequentially (at least three), based on established technologies. Our analysis strongly suggests that risk reduction through sequential deployments of existing technology in the UK can drive output energy costs down by as much as 45%, largely through a combination of increased scale, infrastructure sharing and reductions in financing costs. This paves the way for the introduction of higher risk emerging technologies once the overall CCS risk is reduced.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.045 | 0.017 |
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".