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Record W3135290002 · doi:10.1115/1.4049582

Performance Investigation of Solar Organic Rankine Cycle System With Zeotropic Working Fluid Mixtures for Use in Micro-Cogeneration

2021· article· en· W3135290002 on OpenAlexafffund
Wahiba Yaïci, Evgueniy Entchev, Pouyan Talebizadehsardari, Michela Longo

Bibliographic record

VenueJournal of Energy Resources Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicThermodynamic and Exergetic Analyses of Power and Cooling Systems
Canadian institutionsNatural Resources Canada
FundersNatural Resources Canada
KeywordsOrganic Rankine cycleZeotropic mixtureCogenerationProcess engineeringDegree RankineRenewable energyWaste managementWaste heatEnvironmental scienceElectricity generationThermodynamicsHeat exchangerEngineering

Abstract

fetched live from OpenAlex

Abstract Overall, there are numerous sustainable sources of renewable, low-temperature heat, principally solar energy, geothermal energy, and energy produced from industrial wastes. Extended utilization of these low-temperature alternatives has a certain capacity of decreasing fossil fuel use with its associated very hazardous greenhouse gas emissions. Researchers have commonly recognized the organic Rankine cycle (ORC) as a feasible and suitable system to produce electrical power from renewable sources based on its advantageous use of volatile organic fluids as working fluids (WFs). Researchers have similarly shown an affinity to the exploitation of zeotropic mixtures as ORC WFs due to their capability to enhance the thermodynamic performance of ORC systems, an achievement supported by improved fits of the temperature profiles of the WF and the heat source/sink. This paper determines both the technical feasibility and the benefits of using zeotropic mixtures as WFs by means of a simulation study of an ORC system. This study analyzes the thermodynamic performance of ORC systems using zeotropic WF mixtures to produce electricity driven by low-temperature solar heat sources for use in buildings. A thermodynamic model is created with an ORC system with and without a regenerator. Five zeotropic mixtures with diverse compositions between 0 and 1 in 0.2 increments of R245fa/propane, R245fa/hexane, R245fa/heptane, pentane/hexane, and isopentane/hexane are assessed and compared with identify the best blends of mixtures that are able to produce superior efficiency in their system cycles. Results disclosed that R245fa/propane (0.4/0.6) with regenerator produces the highest net power output of 7.9 kW and cycle efficiency of 9.4% at the operating condition with a hot source temperature of 85 °C. The study also investigates the effects of the volume flow ratio, and evaporation and condensation temperature glide on the ORC’s thermodynamic performance. Following a thorough analysis of each mixture, R245fa/propane is chosen for a parametric study to examine the effects of operating factors on the system’s efficiency and sustainability index. It was found that the highest cycle efficiency and highest second law cycle efficiency of around 10.5% and 84.0%, respectively, were attained with a mass composition of 0.6/0.4 at the hot source temperature of 95 °C and cold source temperature of 20 °C with a net power output of 9.6 kW. Moreover, results revealed that for zeotropic mixtures, there is an optimal composition range within which binary mixtures are tending to work 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 in the range 3.1–9.8% versus 8.6–17.4% for ORC both without and with regeneration, respectively. In conclusion, utilizing zeotropic mixtures may well expand the restriction faced in choosing WFs for solar-powered ORC-based micro-combined heat and power (CHP) systems.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.172
Teacher spread0.166 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2021
Admission routes2
Has abstractyes

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