Heat Pump-Organic Rankine Cycle Hybrid Systems for Co/Tri-Generation Applications: A State-of-the-Art Overview
Bibliographic record
Abstract
Abstract The following paper aims to explore a heat pump’s (HP) as well as an organic Rankine cycle’s (ORC) novel combination for the development of both an efficient and low-emissions heating and cooling systems. This latest review examines both benefits and possibilities of a combined HP-ORC system. Previously, studies have explored several different combinations, such as directly-coupled and reversible combination units as well as parallel configurations units in addition to indirectly-coupled ones. Following defining aforementioned configurations, a discussion on their performance is carried out in detail. Considerations for the optimisation of the architecture, overall of such hybrid systems via utilising the same sources while also discussing heat source, sink selection and operating temperatures as well as thermal energy storage, expander/compressor units, control strategies in addition to working fluids’ selection and managing seasonal temperatures that are increasingly variable, have been identified. Additionally, the experimental studies that have been performed reveal increasingly practical obstacles as well as other areas that require more research while serving to shed light on experimental techniques, which can be applicable to this research’s area. Based upon research, it has been revealed that regional conditions including temperatures and annual weather as well as the cost of energy produce a colossal effect on such systems’ economic feasibility framework as well as partially dictating the overall system configuration’s selection. Additionally, the review disclosed how important the following elements are: 1) a greater temperature differential amid the source of heat and heat sink; 2) proper source of heat and sink selection; 3) working fluid selection; and 4) thermal storage for the maintenance of the difference. Comparatively, from the research works from the past, additional optimisation based on individual component level as well as through control strategies of either an advanced or predictive method, these produce a smaller effect and are worth performing an evaluation on economically due to them not being feasible for the current system. Lastly, based on investigated research, there are certain areas for which recommendation have been provided with regard to future research and this includes a technology configurations’ comparison for understanding different regions’ optimal system, a sensitivity analysis for understanding key system elements for both optimisation as well as design, both an investigation as well as testing carried out for available units and applicable systems that are presently available, and identifying novel use cases.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".