Developing a carbon negative gas turbine based on chemical looping combustion
Bibliographic record
Abstract
Carbon Capture and Storage is a technology of paramount importance for Sustainable Development Goal 7 (Affordable and Clean Energy) and Sustainable Development Goal 5 (Climate Action). The European Union is moving rapidly towards low carbon technologies, see the Energy Union Strategy. Coupling biofuels and carbon capture and storage to decarbonize the power and the industrial sector can be done through the development of BECCS (Bioenergy with Carbon Capture and Storage). However there are some technical barriers to the development of this technology. If a Chemical Looping Combustion (CLC) plant has to be coupled with a gas turbine, it has to work in pressurized conditions. The effect of pressure on chemical reactions and fluidized bed hydrodynamics, at the moment, is not clear. The paper presents Aspen modeling of energy integration to achieve sufficiently high electrical efficiency. Data are both atken from previous work of the research group at Instituto de Carboquimica, Zaragoza Spain and current experimental campaigns running on pressurised micro reactors and batch fluidised beds. The case is implemented in Aspen based on the “Solids” template: “Solids with metric units”. Firstly is defined the air reactor in which two materials are used: iron and air. The the air reactor is connected to a cyclone and the same is for the fuel reactor. The two modules are interconnected to form a single combustor. The exit of the air reactor is linked to the gas turbine and the hot air expanding in the gas turbine is reused to heat up water to be used to produce steam for a steam turbine. Different cases are evaluated with hydrogen combustor or oxyfuel combustion process to increase turbine inlet temperature. Also different pressures are evaluated to increase the final efficiency of the plant. Accurate considerations are also done on the final costs of the plants configurations comparing coupled air and fuel reactor with the possibility to integrate them in a sole reactor following recent development on PFIR reactor and ICR reactors developed respectively by Canmet Canada and NTNU.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".