Selection of Optimum Working Fluid for Organic Rankine Cycles by Exergy and Exergy-Economic Analyses
Bibliographic record
Abstract
The thermodynamic performance of a regenerative organic Rankine cycle that utilizes low temperature heat sources to facilitate the selection of proper organic working fluids is simulated. Thermodynamic models are used to investigate thermodynamic parameters such as output power, and energy efficiency of the ORC (Organic Rankine Cycle). In addition, the cost rate of electricity is examined with exergo-economic analysis. Nine working fluids are considered as part of the investigation to assess which yields the highest output power and exergy efficiency, within system constraints. Exergy efficiency and cost rate of electricity are used as objective functions for system optimization, and each fluid is assessed in terms of the optimal operating condition. The degree of superheat and the pressure ratio are independent variables in the optimization. R134a and iso-butane are found to exhibit the highest energy and exergy efficiencies, while they have output powers in between the systems using other working fluids. For a source temperature was equal to 120 °C, the exergy efficiencies for the systems using R134a and iso-butane are observed to be 19.6% and 20.3%, respectively. The largest exergy destructions occur in the boiler and the expander. The electricity cost rates for the system vary from 0.08 USD/kWh to 0.12 USD/kWh, depending on the fuel input cost, for the system using R134a as a working fluid.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".