Evaluation of water/energy intensity of green hydrogen production plants in Africa scenario
Bibliographic record
Abstract
Abstract The recent environmental concerns due to CO 2 emissions continuous growth and the contemporary increase in fossil fuel prices on international markets are two important factors that are moving the interest towards green and carbon free fuels. In this sense, green hydrogen production from electrolysis is a very promising option as a way to store electrical energy from renewable energy sources (RES) as fuel. However, two inputs are necessary: electrical energy and water. Whereas in EU scenario, electrical energy costs are the ones which affect more the feasibility, in Africa scenario, the availability of RES, in particular solar, is higher in many Countries, allowing for lower energy costs. Green hydrogen production can represent an important resource for microgrids and remote local communities, where the electrical and gas grids are not well developed. However, in this scenario, the large amount of high purity demineralized water required for the process may represent a critical aspect that must be considered. In this study, three different microgrids located in Africa (Kenya, Mali and South Africa) are analysed, considering solar PV installation, three different water intake options (ground water, surface water and seawater), and the impact of the water purification process on the whole plant from both the energy and the economic standpoints. The analysis is performed for the three scenarios, assuming the same electrolyser size (1 MW), considering PEM commercial systems and evaluating the feasibility in the three scenarios, optimizing the PV plant size (range 1-10 MW) to minimize H2 production cost. For the chosen configurations, the water-energy-food nexus is investigated, as both the water intensity and the required area (not available for agriculture purpose) are evaluated.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".