Experimental integration of a foam-based floating photovoltaic (floatovoltaic) system with an anion exchange membrane electrolyzer for 5 kW-Scale green hydrogen production
Bibliographic record
Abstract
Strategically scheduling electrolyzers to harness surplus solar photovoltaic (PV) energy decreases reliance on the grid and enhances overall system efficiency. This study experimentally evaluates a 7-kW foam-based FPV integrated with a 27-cell anion exchange membrane (AEM) electrolyzer to assess feasibility under off-grid conditions. The methodology involved assembling the FPV modules on a pond, powering the AEM stack via three 2.5 kW inverters and MPPT charge controllers, and recording operational data such as voltage, current, temperature, and gas flow; using a Cerbo-GX monitor, multimeters, rotameters, and liquid-displacement timing. Key findings include a stack-level energy conversion efficiency of 73.3–86.2 % (high heating value basis), a minimum specific energy consumption of 45.77 kWh/kg H 2 , and hydrogen purity of 99.22 %. System-level electrical efficiency ranged from 66 % to 71 %, with power conversion losses identified at the inverter and power-supply stages. Simulation of electrolyzer scheduled operation only upon surplus PV generation showed improved energy utilization. These results demonstrate the viability of FPV-AEM coupling for decentralized green hydrogen production and highlight the potential for direct DC coupling and enhanced thermal management to further reduce energy losses in future implementations.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".