Analysis of binary cycle efficiency using Redlich–Kwong equation of state
Bibliographic record
Abstract
Coal, natural gas, and nuclear power plants operate using various forms of Rankine cycle. A variant of Rankine cycle known as binary cycle has been implemented in power plants; yet, the optimization of binary cycles remains relatively unexplored. This article is concerned with efficiency maximization of binary cycle by investigating various working fluid materials for different operating conditions. We introduce a novel analysis approach by employing Redlich–Kwong equation of state to derive a simple, effective way to express the efficiency of binary cycles suitable for most working fluids. Five alkali metals and mercury are investigated for the topping cycle and two fluids for the bottoming cycle. The results show that for operating conditions of 1.25 MPa, 811 K, and 458 kPa, 589 K for the topping cycle and 10.6 MPa, 589 K, and 1.71 kPa, 289 K for the bottoming cycle, the mercury/ammonia combination provides the highest efficiency. For operating conditions of 1.25 MPa, 1123 K, and 458 kPa, 823 K for the topping cycle and 29 MPa, 823 K, and 4 kPa, 295 K for the bottoming cycle the mercury/ammonia combination also provides the highest efficiency. These binary cycle efficiencies are significantly higher than comparative Rankine cycle efficiencies.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".