Techno-Economic Comparison Of The ORC And The PEORC For Low-Temperature Industrial Waste Heat Recovery
Bibliographic record
Abstract
In this paper, the Organic Rankine Cycle (ORC) and the Partially Evaporated Organic Rankine Cycle (PEORC) are techno-economically compared for low-temperature waste heat recovery, with a particular focus on industrial applications.Numerical models of the two power cycles were developed, while a dedicated two-phase expansion model simulating the performance of an industrial expander in the two-phase region was applied to estimate more precisely the efficiency of the PEORC.Different WFs, temperatures of the heat source, and waste heat transfer rates were considered for a complete mapping of the power cycles' efficiency.The PEORC power cycle simulations indicate that its heat-to-power efficiency is highly dependent on the performance of the two-phase expander, with vapor quality at the evaporator outlet identified as the most crucial operating parameter.The efficiency comparison between the two alternative power cycle architectures reveals that the PEORC performs consistently better, achieving thermal efficiencies between 2.28% and 7.75%, whereas the respective values for the ORC are in the range of 1.25% to 7.13%.Both the ORC and the PEORC demonstrate favorable financial performance for the studied operating conditions.By applying the PEORC, the Levelized Cost Of Electricity (LCOE) for the industry is expected to fluctuate between 0.015 and 0.119 €/kWh, 16-17% lower than the values estimated with the ORC.Favorable PayBack Periods (PBP) (4-5 years) and Net Present Values (NPV) (260-480k€) are expected when the PEORC is applied, always higher than the respective values for the ORC because of its increased energy efficiency.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.007 | 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".