Energy Storage and Direct Air Carbon Capture Solution for Offshore Sources of Energy
Bibliographic record
Abstract
Abstract Power intermittency associated with many forms of renewable energy has been a significant challenge to overcome since its increased contribution to our grids. Additionally, the global climate crisis driven by increased greenhouse gasses in the atmosphere has driven innovations into Direct Air Capture (DAC) technology which removes carbon dioxide directly from the air. The present report outlines a theoretical design for an offshore energy storage system which seeks to provide solutions to both of these challenges in a consolidated system. The proposed system involves a motorized cable system built around an offshore wind turbine. Attached to the cable is a series of air balloons or “gondolas”. During times of excess energy production by the turbine, these gondolas are driven to a depth of 400 meters below the ocean surface. When energy demand increases, gondolas are released back up to the surface wherein a net upwards buoyant force recovers a portion of the energy input. Additionally, at maximum depth, hydrostatic pressure is large enough to condense the carbon dioxide content in the air, and the system is designed to separate and store it. Iterative analysis indicates a system efficiency of 92.83% with a storage capability of 16.29MWh and a carbon recovery rate of 17.95 kg per cycle. Through cost analysis, the NPV of the system is found to vary dependent on implementation location and governmental incentives for a set payback period, ranging between $1.1 million and $3.1 million across Vancouver, Canada and Oslo, Norway. This technology has the potential to be very competitive with conventional Battery Energy Storage Systems (BESS) and Flywheel Energy Storage Systems (FESS). In addition to this system design, our team has designed and will construct a prototype for one gondola which is planned to be field tested in the Summer of 2023.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".