Thermally driven reverse osmosis: thermodynamics of a novel process that uses heat for desalination and water purification
Bibliographic record
Abstract
Thermal energy is available from a variety of renewable sources, and can be an important energy source for sustainable water desalination, treatment, and reuse. In this study, we describe the thermodynamic limits of a novel thermally driven reverse osmosis (TDRO) process and compare its performance to other water separation technologies. The proposed TDRO system consists of a piston set that uses the thermal expansion of a saturated working fluid to act on an impaired feed water source to drive clean water permeate across a RO membrane. Applying the first and second laws, we show that the amount of heat needed is highly sensitive to feed concentration, recovery ratio, selection of the working fluid, operating temperature, and size of the working piston. A minimum specific heat of 20 kWh/m 3 is achieved for 50 % recovery of a seawater feed source, when (i) water is selected as the working fluid, (ii) the working fluid is operated at 247 °C, and (iii) the piston is sized properly. This translates to a first law efficiency of 7.9 %, a second law efficiency of 18.1 %, and a gain output ratio above 32. At lower working temperatures of 110 °C, specific heat increases slightly to 24 kWh/m 3 and the gain output ratio drops to 26. These metrics compare favorably with other thermal separation technologies, suggesting that TDRO can be an important means of harnessing low-grade heat for water production.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".