Thermoeconomic analysis and multiobjective optimization of a combined gas turbine, steam, and organic Rankine cycle
Bibliographic record
Abstract
Abstract Because of the fossil fuels crisis in recent years, efficient working of power producing cycles has gained considerable importance. This study presents a detailed exergoeconomic analysis of a proposed combination of a gas turbine ( GT ), a steam Rankine cycle ( SRC ), and an organic Rankine cycle ( ORC ), which are coupled together to obtain the maximum heat recovery of the GT exhaust gas. The proposed cycle was analyzed from both thermodynamic and economic viewpoints. The exergy efficiency and product cost rate of the introduced cycle were optimized simultaneously using multiobjective optimization with seven decision variables, including steam turbine inlet pressure and temperature, ORC turbine inlet pressure, ORC and steam turbine back pressures, and pinch point of heat exchangers. Sensitivity analysis revealed that the steam turbine back pressure and inlet pressure had the highest impact on product cost rate and exergy efficiency, followed by ORC turbine inlet pressure and back pressure. Also, the exergoeconomic analysis showed that the combustion chamber had the highest sum of exergy destruction costs and investment costs; more attention should thus be paid to its design procedure. Under the design conditions, the exergy efficiency of 40.75% and product cost rate of 439 million $/year could be achieved.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".