Using Green Energy Sources in Trigeneration Systems to Reduce Environmental Pollutants: Thermodynamic and Environmental Evaluation
Bibliographic record
Abstract
With rising electricity demand and environmental concerns, renewable energy is increasingly important. Geothermal power plants offer an opportunity to utilize natural energy sources advantageously. These systems can be coupled with other power cycles, like gas Brayton cycles, to maximize their potential output. Biogas is considered a viable replacement for fossil fuels such as natural gas to further mitigate pollutant gas emissions. In this paper, a biogas-fueled gas turbine coupled with a double-expansion geothermal cycle is proposed that uses, for heat recovery, combustion product gases to run a Kalina cycle. After heating the geothermal fluid twice for double expansion, the product hot gases also heat water in a domestic water heater. Also, three thermoelectric generators are utilized to increase the overall output. Using the geothermal cycle’s waste heat, a humidifier–dehumidifier desalination unit is considered for freshwater production. Green energy, freshwater, and heat are the system’s products, all of which are useful. The proposed system is examined from a thermodynamic perspective using EES V.10.561 (Engineering Equation Solver) software. For the considered input parameters, energy and exergy efficiencies of 36% and 44% are achieved.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".