Modified Solution–Diffusion Model Incorporating Rotational Kinetic Energy in Pressure Retarded Osmosis
Bibliographic record
Abstract
Pressure-retarded osmosis (PRO) is a process that allows the production of mechanical energy from the chemical potential difference between two solutions of different concentrations separated by a semi-permeable membrane. One of the main obstacles for this technology to be commercially competitive is the difference between the theoretical power density and the experimental power density due to negative factors like ICP. Analytical models facilitate the analysis of the relationships between system parameters and thus facilitate the optimization of components. In general, PRO has traditionally been explained through the solution–diffusion model, where the flow of water through the membrane depends on a diffusivity factor, the concentration gradient, and the hydraulic pressure gradient. This paper focuses on developing a modified solution–diffusion model that includes means to control the ICP through rotational kinetic energy. An energy balance method for obtaining a solution diffusion-based model is explained, and an analytical model is obtained. Finally, said model is verified through simulations with parameters reported in the literature to obtain insight on the required dimensions for a prototype. It was found that a turning radius of 0.5 m and an angular speed of less than 3000 rev/min could generate enough kinetic energy to compensate for ICP losses in a PRO scenario. Also, the results suggest that bigger concentration differences could benefit more of this technology, as they require almost the same energy as smaller concentration differences but allow for more energy extraction.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".