FUEL CELL HEAT RECOVERY, ELECTRICAL LOAD MANAGEMENT, AND THE ECONOMICS OF SOLAR-HYDROGEN SYSTEMS
Bibliographic record
Abstract
Computer modelling of a solar-hydrogen system to supply a remote household in southeast Australia has been conducted. Electrical load management and fuel-cell heat recovery have been investigated to improve the system's economy. The results reveal that the cost of the solar hydrogen system can be reduced by over 10% by managing the peak demand and accordingly the capacity of the fuel cell while keeping the average daily electrical energy supplied constant. Interestingly, increasing the size of the fuel cell up to a certain level above the minimum required actually lowers the average unit cost of energy supplied since the fuel cell operates at lower current densities and hence better efficiency. A smaller PV array, electrolyser and hydrogen tank are then required as well. Heat recovered from the fuel cell and used to substitute for LPG in a domestic hot water unit could lead to a further reduction in the overall capital cost of the household's energy system. While the recoverable heat available was found to be less if optimal load management is also applied, there remained a net economic benefit of supplying both heat and power.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".