COMPUTER SIMULATION OF HYDROGEN (H2) ENERGY FUELS CELL TECHNOLOGY ADOPTION FOR ELECTRICITY-POWER GENERATION IN THE COASTAL REGION OF ONDO STATE
Bibliographic record
Abstract
Nigeria’s coastal regions, including Ondo State, experience severe electricity deficits due to unreliable grid infrastructure and dependence on fossil fuels. This study investigates the adoption of hydrogen (H₂) fuel cell technology as a clean and scalable alternative for power generation in these areas. Using simulations developed in Python and MATLAB, hydrogen fuel cell performance was modeled under varying flow rates (25–50 L/h) and environmental conditions. Results show a linear increase in power output (13.9–27.9 kW) with stable conversion efficiency (approximately 60%), indicating reliability and scalability for rural and industrial applications. Comparative analysis suggests hydrogen fuel cells can reduce greenhouse gas emissions, operational costs, and environmental degradation compared to diesel-powered systems. Techno-economic and policy assessments highlight long-term cost savings, energy security, and social benefits when integrated with solar-powered hydrogen production. The study concludes that hydrogen fuel cells offer a viable, sustainable pathway for clean electricity generation and energy transition in Ondo State’s coastal regions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".