Optimization of a cavern‐based compressed air energy storage facility with an efficient adaptive genetic algorithm
Bibliographic record
Abstract
Abstract Due to the dynamic interactions of the components of cavern‐based compressed air energy storage plants, optimizing this system is challenging and a small change in the design parameters, such mass flow rate, compression ratio, expansion ratio can significantly alter the efficiency of the entire system. An adaptive genetic algorithm has been invoked to overcome this challenge, with system efficiency and exergy efficiency as the objective functions. The proposed method provides more flexibility to the optimization process; instead of using fixed rates for the mutation percentage, it is adjusted individually based on the feedback from both the intensity of a component's distributions in the design space and the relative objective function of that component in comparison to other components. The method is utilized for the efficiency optimization of an 80 MW plant with 9° of freedom. For performing the optimization process an automatic coupling between HYSYS and MATLAB was used. The proposed search algorithm discovered a significantly wider range of data by increasing the chance of design parameters relocation with efficient shifting of the searching domain. Outcomes indicated the merit of the algorithm by 6.4% increase in the efficiency of the plant as well as notably decreasing the number of objective function evaluations.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".