Assessing process feasibility of salinity gradient systems through maximum extractable and net energy outputs
Bibliographic record
Abstract
Salinity gradient (SG) has long been considered a promising renewable energy source. Three key technologies, pressure retarded osmosis (PRO), reverse electrodialysis (RED), and nanopore-based power generation (NPG), have been extensively investigated for harnessing SG energy, though their viability remains debated. In this study, to advance the discussion, we derived equations to quantify the maximum extractable energy from PRO, RED, and NPG in module-scale operations under co-current and counter-current modes to estimate the theoretical energy recovery potential of each technology from SG. Results indicate that, under ideal conditions, PRO outperforms RED and NPG in energy efficiency. For real applications, the net energy output (NEO) of SG power plants was assessed using reverse osmosis brine (1.2 M) as the highly concentrated solution and wastewater effluent as the low concentrated solution. Accounting for energy losses, less than 10% of the maximum theoretical recoverable energy is captured as NEO for RED and NPG, while PRO achieves 37%. Furthermore, considering various salinities of highly concentrated solution, a techno-economic analysis was performed to support the theoretical findings. It revealed that the levelized cost of energy (LCOE) for RED and NPG remains prohibitively high (LCOE > 0.6 $/kWh) even for hypersaline solutions ( C > 3.5 M). In contrast, PRO demonstrated a more favorable economic outlook, with LCOE approaching competitiveness at 0.26 $/kWh at high salinity. Lastly, pathways to mitigate energy losses and improve process feasibility are proposed to guide future development of economically viable SG energy systems.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".