Waste heat recovery of industrial exhaust gas : study of variable powerejector cooling systems
Bibliographic record
Abstract
Large quantities of energy are wasted in the form of heat. For a given waste energy source, when its temperature is high enough, a desirable recovery strategy is a first Organic Rankine Cycle (ORC) stage for electricity production, followed by using the remaining available energy, at lower temperature, for either building heating in winter or building cooling in summer. Thermal cooling can be achieved with an ejector cycle. However, a single ejector having a fixed geometry does not have the flexibility to follow the variation of the building cooling load, especially if the outside temperature, and thus the condensing pressure, fluctuates. The proposed solution is to use multi-ejector blocks, designed to handle the changing conditions. This theoretical study used a thermodynamic model built with Python that allowed to model the different multi-ejector design. The first multi-ejector block is one of scaled ejectors designed to provide cooling at the same condensing pressure. The second is the use of different geometry ejectors designed to handle variable condensing pressures. This study explores a range of operating conditions that could be obtained by using waste heat or heat of renewable sources. The simulations were done for a cycle with R-600a, a natural refrigerant showing the potential to provide high system flexibility. The results show that for fixed operating pressures with variable cooling load requirements, the simultaneous use of scaled ejectors would be preferred over the use of different geometry ejectors.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".