Performance analysis of the Thermo Osmotic Energy Conversion (TOEC) process for harvesting low-grade heat
Bibliographic record
Abstract
Low-grade heat energy from resources below 100 °C is readily available in massive quantities worldwide. However, existing technologies face challenges in converting this heat power into usable forms of energy, such as electricity. This is primarily due to temperature fluctuations in these heat sources and the limited temperature gradient with the surrounding environment. To address this issue, a recently developed technology called thermo-osmotic energy conversion (TOEC) offers a promising solution for harvesting electrical energy from low-grade heat sources. In the TOEC method, a hydrophobic membrane facilitates the transfer of water vapor molecules from a moderately hot aqueous solution to a cold water stream. The resulting hydraulic pressure in the compressed water on the cold side can be harnessed using a hydro-turbine. Despite the many interesting features of TOEC technology, a comprehensive analysis of its performance under various operating conditions and membrane properties is currently lacking in the literature. In this study, we conducted a theoretical evaluation of the TOEC process based on mass and heat transfer phenomena, and we validated our findings with experimental data. Our results indicate that employing membranes with smaller pore size, low thickness, and high porosity, along with higher feed temperature and flowrates, can significantly enhance energy efficiency and power density. Specifically, we demonstrate that the utilization of hydrophobic membranes with nanometer-sized pores, coupled with hydraulic pressures ranging from 6.2 bar to 11.8 bar, enables us to achieve power densities exceeding 5 W/m2, given a 20 °C heat sink and a heat source temperature above 65 °C. Furthermore, we have determined that an applied hydraulic pressure of 9.4 bar yields the maximum energy efficiency value of 0.016%.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".