Thermo-economic assessment and optimization of a multigeneration system powered by geothermal and solar energy
Bibliographic record
Abstract
A novel multigeneration system using dual renewable energy sources (i.e., geothermal and solar) is introduced, analyzed, and optimized. The integration of a geothermal line, a solar tower, a steam Rankine cycle, two organic Rankine cycles, an ejector refrigeration cycle, a thermoelectric generator unit, and a reverse osmosis subsystem forms the entire system. The outputs of this energy-conversion system are heating load, cooling load, electricity, and freshwater. Regarding methodology, the energy, exergy, and exergoeconomic approaches are implemented to assess the system from thermodynamic and economic viewpoints. Moreover, an optimization process based on exergy efficiency and the total unit cost of products is executed to determine the system’s optimal decision variables. The results obtained from the optimization process show that the proposed system is able to achieve 25.4% exergy efficiency and 34.1 $/GJ total unit cost of products, exhibiting 48% and 43% improvement compared to a base case study. Furthermore, the methodology is demonstrated on a case study where the system operates at its optimum condition in a specific location. Having monthly average values of direct normal irradiation for this spot, the average hourly performance of the system is evaluated for each month. Based on the obtained results, the minimum and maximum freshwater production rates are 3.06 kg/s and 3.84 kg/s, respectively. It can be estimated that a range of 1224 to 1536 individuals, varying from month to month, can receive the produced freshwater.
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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) | 0.001 | 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) | 0.001 | 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".