Towards a Clean and Sustainable Distributed Energy: The Potential of Integrated PEMFC-CHP
Bibliographic record
Abstract
The use of fossil fuels within the current infrastructure for domestic energy supply is one of the main causes of anthropogenic emissions. The mitigation options to meet the ambitious carbon reduction targets set by the UK government are discussed in this paper, including the use of carbon capture and storage technology, clean renewable energy integration and a proposed system of integrated fuel cell combined heat and power (FC-CHP) technology. Analysis shows that the use of carbon capture and storage (CCS) technology within the current infrastructure can abate half the electricity associated CO2 emissions; however, this comes at a high cost penalty. The emissions associated with domestic heat cannot be prevented without changes in the energy infrastructure. Hydrogen powered fuel cells can provide clean energy at a range of scales and high efficiencies, especially when employed with a CHP system. However, production of CO2 free hydrogen is essential for fuel cell technology to contribute substantially to a low carbon economy globally. In this work three methods were investigated for small scale distributed hydrogen production, namely steam methane reforming, water electrolysis and cold plasma jet. The criteria used for comparisons include the associated CO2 emissions and the cost of energy production. Cold plasma jet decomposition of methane shows a high potential when combined with integrated FC-CHP technology for economically viable and CO2 free generation of energy, especially in comparison to water electrolysis. Including the value of the solid carbon product makes the plasma system most attractive economically.
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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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".