Multi-Objective Optimization of a Geothermal Steam Turbine Combined With Reverse Osmosis and Multi-Effect Desalination for Sustainable Freshwater Production
Why this work is in the frame
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Bibliographic record
Abstract
Abstract A novel geothermal desalination system is proposed and optimized in terms of maximizing the exergy efficiency and minimizing the total cost rate of the system. The system includes a geothermal steam turbine with a flash chamber, a reverse osmosis unit, and a multi-effect distillation system. First, exergy and economic analyses of the system are performed using Engineering Equation software. Then, an artificial neural network is used to develop a mathematical function linking input design variables and objective functions for this system. Finally, a multi-objective optimization is carried out using a genetic algorithm to determine the optimum solutions. The Utopian method is used to select the favorable solution from the optimal solutions in the Pareto frontier. Also, the distributions of the values of design variables within their allowable ranges are investigated. It is found that the optimum exergy efficiency and total cost rate of the geothermal desalination system are 29.6% and 3410 $/h, respectively. Increasing the seawater salinity and decreasing the intake geothermal water temperature result in an improvement in both exergy efficiency and total cost rate of the system, while variations in the flash pressure and turbine outlet pressure lead to a conflict between the exergy efficiency and the total cost rate of the geothermal desalination system over the range of their variations.
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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 | 0.000 | 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 | 0.000 |
| 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 it