Exergy Analysis of an Industrial Waste Heat Recovery Based Cogeneration Cycle for Combined Production of Power and Refrigeration
Bibliographic record
Abstract
In this paper, a novel industrial waste heat recovery based cogeneration is proposed for the combined production of power and refrigeration. The system is an integration of Rankine power cycle and absorption refrigeration cycle. A thermodynamic analysis through energy and exergy is employed, and a comprehensive parametric study is performed to investigate the effects of exhaust gas inlet temperature, pinch-point, and gas composition on energy efficiency, power-to-cold ratio, and exergy efficiency of the cogeneration cycle and exergy destruction in each component. The variation in specific heat with exhaust gas composition and temperature is accounted in the analysis for further discussion. The first-law efficiency decreases while power-to-cold ratio and exergy efficiency increase with increasing exhaust gas inlet temperature. The parameters, such as power-to-cold ratio and second-law efficiency, decrease while first-law efficiency increases with increasing pinch-point. Exergy efficiency significantly varies with gas composition and oxygen content of the exhaust gas. Approximating the exhaust gas as air, and the air standard analysis leads to either underestimation or overestimation of cogeneration cycle performance on exergy point of view. Exergy analysis indicates that maximum exergy is destroyed during the steam generation process; which represents around 40% of the total exergy destruction in the overall system. The exergy destruction in each component of the system varies significantly with exhaust gas inlet temperature and pinch-point. The present analysis contributes further information on the role of composition, exhaust gas temperature, and pinch-point influence on the performance of a waste heat recovery based cogeneration system from an exergy point of view.
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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 | 0.000 |
| 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) | 0.001 | 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".