Performance Analysis of Regenerative Organic Rankine Cycle System for Solar Micro Combined Heat and Power Generation Applications
Bibliographic record
Abstract
The recurrent rises in energy demand and greenhouse gas emissions (GHGs) appeal for effective usage of energy sources. Micro-combined heat and power (micro-CHP) generation is regarded as an efficient replacement to traditional energy systems with distinct electrical and thermal production attributable to the greater energy effectiveness, reduced capacity and to the reduced GHGs. In this context, the Organic Rankine Cycle (ORC) is broadly recognised like a capable system to generate electrical power from solar energy, waste heat or lowquality thermal energy sources, even lower than 90 oC. The present study aims at examining the performance of a solar driven micro-CHP system for residential buildings using a regenerative ORC. The analysis focuses on modelling, simulation and optimisation of various working fluids (WFs) in ORC to utilise low-temperature heat source from solar thermal collectors for heat and power generation. A detailed parametric study is performed to analyse the impacts of different WFs and operating situations at several temperatures of the hot and cold sources, as well as several temperatures and flow rates of the evaporator heating and condenser cooling WFs, on the system performance and heating and electrical power yields. The outcomes showed significant changes in performance such as efficiency and power extracted by the expander and generator based on the temperatures of each hot or cold sources for all WFs. The work extracted by the expander and the electrical power were within the range for residential building applications, in the range of 1-7 kWe, with an electrical isentropic efficiency of about 60% and cycle efficiency up to 9.8%, for a hot source temperature of 108 oC. The WFs will operate in the hot source temperature range that would allow the use of a solar flat plate or evacuated tube collectors.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".