Performance Investigation of Solar Organic Rankine Cycle Systems With and Without Regeneration and With Zeotropic Working Fluid Mixtures for Use in Micro-Cogeneration
Bibliographic record
Abstract
Abstract Globally there are several viable sources of renewable, low-temperature heat (below 130°C) particularly solar energy, geothermal energy, and energy generated from industrial wastes. Increased exploitation of these low-temperature options has the definite potential of reducing fossil fuel consumption with its attendant very harmful greenhouse gas emissions. Researchers have universally identified the organic Rankine cycle (ORC) as a practicable and promising system to generate electrical power from renewable sources based on its beneficial use of volatile organic fluids as working fluids (WFs). In recent times, researchers have also shown a preference for/an inclination towards deployment of zeotropic mixtures as ORC WFs because of their capacity to improve thermodynamic performance of ORC systems, a feat enabled by better matches of the temperature profiles of the WF and the heat source/sink. This paper demonstrates both the technical feasibility and the notable advantages of using zeotropic mixtures as WFs through a simulation study of an ORC system. The study examines the thermodynamic performance of ORC systems using zeotropic WF mixtures to generate electricity driven by low-temperature solar heat source for building applications. A thermodynamic model is developed with an ORC system both with and excluding a regenerator. Five zeotropic mixtures with varying compositions of R245fa/propane, R245fa/hexane, R245fa/heptane, pentane/hexane and isopentane/hexane are evaluated and compared to identify the best combinations of WF mixtures that can yield high efficiency in their system cycles. The study also investigates the effects of the volumetric flow ratio, and evaporation and condensation temperature glides on the ORC’s thermodynamic performance. Following a detailed analysis of each mixture, R245fa/propane is selected for parametric study to examine the effects of operating parameters on the system’s efficiency and sustainability index. For zeotropic mixtures, results showed that there is an optimal composition range within which binary mixtures are inclined to perform more efficiently than the component pure fluids. In addition, a significant increase in cycle efficiency can be achieved with a regenerative ORC, with cycle efficiency ranging between 3.1–9.8% and 8.6–17.4% for ORC both without and with regeneration, respectively. Results also showed that exploiting zeotropic mixtures could enlarge the limitation experienced in selecting WFs for low-temperature solar organic Rankine cycles.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".