Novel hybridization concept for efficient performance of a peak-shaving technology by adsorption and liquid air energy storage systems
Bibliographic record
Abstract
Innovative ideas in energy system engineering with practical methodologies can pull the trigger for sustainable solutions on novel pathways. This study presents the results of a comprehensive investigation of a novel combined large-scale energy storage technology with a thermal energy reservoir. In the design stage, the sorption thermal energy is employed as the supplementary energy package in an integrated configuration with LAES and a hybrid desalination system. The technical, economic, and other combinations of these approaches are used to investigate the performance of the designed multigeneration system for a range of products, such as electrical power and freshwater, in the case of need. The soft-computing optimization powered by the Grey Wolf algorithm improved the exergetic effectiveness and the system’s levelized cost of the product from 61.12 % and 0.8 US$/kWh to 63.79 % and 0.369 US$/kWh, respectively. The most considerable improvement has been observed in the levelized cost of produced freshwater from 11.32 to 3.99 US$/m 3 . It is outstanding that the Exergy Destruction Rate (EDR) improved from 15.95 MW to 10.12 MW. The promising cost-effectiveness, productivity, and environmentally-benign nature of the proposed system makes it an interesting option for communities with energy storage concerns.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".