Performance investigation on novel combined power generation and refrigeration system
Bibliographic record
Abstract
This article aims to examine a novel combined power and refrigeration system, using renewable and waste heat sources suitable for low-temperature applications. The present system is an integrated Kalina cycle and ejector refrigeration system to generate power and refrigeration simultaneously. To improve the vapour generation, the separator vapour fraction is used as a decision variable. Relative irreversibility and efficiency defect as two important parameters considered in this system for an investigation to identify the weaker components. The combined system generates power and refrigeration with two different mediums by the incorporation of the heat exchanger at the turbine exhaust. The novel system's energy and conventional exergy evaluation are carried out through Python Software. The optimum values of decision variables: turbine concentration, separator vapour fraction, entrainment ratio, expander ratio, split ratio and turbine concentration are identified using Python software from an opted range of variables. The maximum value of net power output, first law efficiency for power generation system, combined system, second law efficiency for power generation system, combined system, refrigeration effect and coefficient of performance are obtained as 113 kW, 8.85%, 11.83%, 93.44%, 81.29%, 38.07% and 0.118, respectively, at higher separator vapour fraction. Among the components considered in the combined power generation system, the condenser and LTRGN account for the higher exergy destruction rate of 30.41% and 25.53%. The coefficient of performance is maximized at a higher value of the refrigeration effect. The turbine pressure at the inlet is increased with increments in turbine work on choosing the higher value of the expander ratio. The higher exergetic value components are not emphasized to focus on improvement.
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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".