Thermo-economic analysis of a new dual expansion geothermal-LNG cold energy based on multi-generation system with green hydrogen generation
Bibliographic record
Abstract
This study carries out an original thermoeconomic assessment of a hybrid energy system that mixes modified geothermal technology with LNG cold energy to produce sustainable hydrogen. The innovative configuration uses three main parts: a geothermal plant that works in a special way and uses hydraulics instead of usual expansion valves, a water-powered PEM electrolysis plant from a dual-effect desalination facility, and an LNG unit for converting cold energy. Thorough assessments prove that the system has superior technical features, saving 67.4 % of energy and 38.7 % of exergy, as well as producing electricity with 11.01 % efficiency. There is a total of 2.18 MW of exergy destruction, where the desalination part is responsible for approximately half, and the facility remains profitable at $0.021 per kWh by spending $2.02 million a year on operations. Parametric results indicate that increasing the geothermal turbine inlet pressure does not change the exergy efficiency by much and only increases water productivity, while pumping LNG at 70 bar improves all the system’s performance. With these changes, the coefficient of performance goes up to 0.29 and each of the aforementioned efficiencies rises, proving that the solution offered here is practical.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".