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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".