Energetic and techno-economic analysis of harvesting energy from low-grade heat by thermo-osmotic energy conversion
Bibliographic record
Abstract
Low grade heat (LGH) is an abundant source of energy that can be harvested to produce electricity. Several technologies have been developed and investigated to convert LGH to electricity such us thermoelectric generators and organic cycle. Recently, thermo-osmotic energy conversion (TOEC) has been introduced as a potential technology to convert LGH to energy. However, a handful number of works has deeply investigated the theoretical aspect of the process as well as no study has performed a technoeconomic analysis to assess its viability. In the current work, we aim to investigate the viability of TOEC in harvesting electricity from LGH. First, we developed the expressions of the maximum theoretical power density, PD max , and specific energy of TOEC. Then, we investigated the impact of the type of the working fluid as well as the water recovery ratio of the TOEC nanoporous membrane. Theoretical metrics shows that very high-power density ( PD max > 800 W m −2 ) and extractable energy (2.2 kWh m −3 ) are achievable under ideal conditions. However, the results under real conditions showed that the produced energy is not high enough to prove the viability of TOEC(≤ 0.09 kWh m −3 ). In the ideal case, the low produced energy is mainly attributed to low applied pressure, which limits the extractable power. The techno-economic analysis showed, at optimum conditions, that TOEC has a levelized cost of energy LCOE = $0.66/kWh, which is relatively higher than other technologies. Finally, pathways towards TOEC economic feasibility have been discussed to face the challenges that prevent the implementation of a promising and sustainable renewable energy source.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".