Proposal and Comprehensive Analysis of a Novel Combined Plant with Gas Turbine and Organic Flash Cycles: An Application of Multi-Objective Optimization
Bibliographic record
Abstract
Environmental, exergo-economic, and thermodynamic viewpoints are thoroughly investigated for a state-of-the-art hybrid gas turbine system and organic flash cycle. For the proposed system, the organic flash cycle utilizes the waste thermal energy of the gases exiting the gas turbine sub-system to generate additional electrical power. Six distinct working fluids are considered for the organic flash cycle: R245fa, n-nonane, n-octane, n-heptane, n-hexane, and n-pentane. A parametric investigation is applied on the proposed combined system to evaluate the impacts of seven decision parameters on the following key operational variables: levelized total emission, total cost rate, and exergy efficiency. Also, a multi-objective optimization is performed on the proposed system, taking into account the mentioned three performance parameters to determine optimum operational conditions. The results of the multi-objective optimization of the system indicate that the levelized total emission, total cost rate, and exergy efficiency are 74,569 kg/kW, 6873 $/h, and 55%, respectively. These results also indicate the improvements of 16.45%, 6.59%, and 3% from the environmental, economic, and exergy viewpoints, respectively. The findings reveal that utilizing n-nonane as the working fluid in the organic flash cycle can yield the lowest levelized total emission, the lowest total cost rate, and the highest exergy efficiency.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".