Temperature Effects and Entropy Generation of Pressure Retarded Osmosis Process
Bibliographic record
Abstract
Pressure retarded osmosis (PRO) is considered as one of the promising and new techniques to generate power. In this work, a numerical model was used to study the effect of the flow streams temperature on the performance of the PRO process and entropy generation. The variation of the feed solution and draw solution temperatures, pressure difference, concentration difference, and flow rates on the power density and entropy generation were discussed. The model results were validated with experimental measurements obtained from literature and showed a good agreement with the model predictions. It was found that the power density increases by about 130% when both feed solution and draw solution temperatures increase from 20 °C to 50 °C. The feed solution temperature has more impact on the power density than that of the draw solution. This is due to the direct effect of the feed solution temperature on the water permeability and diffusion coefficient. The effect of the feed solution temperature becomes significant at higher concentration differences. Whereas, at low concentrations, the power density slightly increases with the feed temperature. Furthermore, it is found that there is an optimum volumetric flow in the channels that maximizes the power density and minimizes the entropy generation when fixing other operating conditions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".