Thermodynamic, Economic and Sustainability Analysis of Solar Organic Rankine Cycle System with Zeotropic Working Fluid Mixtures for Micro-Cogeneration in Buildings
Bibliographic record
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 suitable system to generate electrical power from renewable sources based on its beneficial usage of volatile organic fluids as working fluids (WFs). In recent times, researchers have also shown a preference towards deployment of zeotropic mixtures as ORC WFs because of their capacity to improve thermodynamic performance of ORC systems, a feat enabled through the greater matching of the temperature profiles of the WF and the heat source/sink. This paper demonstrates the thermodynamic, economic and sustainability feasibility, and the notable advantages of using zeotropic mixtures as WFs through a simulation study of an ORC system. The study examines first the thermodynamic performance of ORC systems using zeotropic mixtures to generate electricity powered by a low-temperature solar heat source for building applications. A thermodynamic model is developed with a solar-driven ORC system both with and excluding a regenerator. Twelve zeotropic mixtures with varying compositions are evaluated and compared to identify the best combinations of mixtures that can yield high performance and high efficiency in their system cycles. The study also examines the effects of the volume flow ratio, and evaporation and condensation temperature glides on the ORC’s thermodynamic performance. Following a detailed analysis of each mixture, R245fa/propane and butane/propane are selected for parametric study to investigate the influence of operating parameters on the system’s efficiency and sustainability index. For zeotropic mixtures, results disclosed 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 substantial enhancement in cycle efficiency can be obtained by 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 revealed that exploiting zeotropic mixtures could enlarge the limitation experienced in selecting WFs for low-temperature solar ORCs. Moreover, a detailed economic with a sensitivity analysis of the solar ORC system was performed to evaluate the cost of the electricity and other economic criteria. The outcome of this investigation should be useful in the thermo-economic feasibility assessments of solar-driven ORC systems using working fluid mixtures to find the optimum operating range for maximum performance and minimum cost.
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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) | 0.000 | 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) | 0.000 | 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".