Transient Thermodynamic Modeling of Heat Recovery From a Compressed Air Energy Storage System
Bibliographic record
Abstract
Abstract Sustainable development calls for a larger share of intermittent renewable energy. To mitigate this intermittency, Compressed Air Energy Storage (CAES) technology was introduced. This technology can be made more sustainable by recovering the heat of the compression phase and reusing it during the discharge phase, resulting in an adiabatic CAES without the need for burning of fossil fuels. The key process parameters of CAES are temperature, pressure ratios, and the mass flow rates of air and thermal fluids. The variation in these parameters during the charge and discharge phases significantly influences the performance of CAES plants. In this chapter, the transient thermodynamic behavior of the system under various operating conditions is analyzed and the impact of heat recovery on the discharge phase energy efficiency, power generation, and CO2 emissions is studied. Simulations are carried out over the air pressure range from 2,500 to 7,000 kPa for a 65 MW system over a five-hour discharge duration. It is also assumed that the heat loss in the air storage and the hot thermal fluid tank is insignificant and standby duration does not impact the status of the system. This result shows that the system exergy and the generated power are more sensitive to pressure change at higher pressures. This work also reveals that every 10°C increase on the temperature of the stored air can lead to a 0.83% improvement in the energy efficiency. The result of the transient thermodynamic model is used to estimate the reduction in CO2 emissions in CAES systems. According to the obtained result, a 65 MW ACAES plant can reduce about 17,794 tons of CO2 emission per year compared to a traditional CAES system with the same capacity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".