Real-Time Emulation of a Pressure-Retarded Osmotic Power Generation System
Bibliographic record
Abstract
Power production by conversion of salt gradient energy (osmotic power production) has the potential for global commercialization. Research on the net-power output and osmotic power plant configurations could result in viable methods for improvement of plant efficiency. In this paper, a novel equivalent electric-circuit model of the pressure-retarded osmosis (PRO) process is described and is used to develop a power-hardware-in-the-loop (PHIL) emulator to represent the osmotic power plant. The model considers many dynamics involved with PRO including reverse salt leakage, concentration polarization, and the salt storage capacity of water. The proposed model facilitates real-time dynamic simulation and analysis of the PRO power plant, and its interaction with the rest of the PRO power system, namely the impulse turbine and the synchronous generator supplying power to off-grid or isolated loads. The response of the multidomain system including hydraulic, mechanical, and electrical components of the system is observed, given changes in the input parameters such as source flow rate. The proposed PHIL emulator provides insight into the operational dynamics and behavior of the PRO system. The proposed real-time emulator serves as a powerful tool that can advance research and development of PRO power generation systems. Simulation and experimental results are presented in this paper to validate the PRO plant model and the operation of the proposed real-time PHIL PRO emulator.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".