Thermoeconomic analysis and multi-objective optimization of a novel trigeneration system consisting of kalina and humidification- dehumidification desalination cycles
Bibliographic record
Abstract
Low-temperature geothermal heat sources have the highest share of geothermal energy in the world. Utilization of these heat sources for energy and freshwater generation can play an important role in meeting energy and freshwater demands. To do so, this study aims to propose a novel trigeneration cycle powered by low-temperature geothermal sources. The proposed system, which is an integration of Kalina and humidification-dehumidification (HDH) cycles, is used for the generation of electricity, heating, and freshwater. For the Kalina cycle, an evaporative condenser is used. It also acts as a humidifier and heater of the humidification-dehumidification desalination cycle, resulting in a reduction in the complexity of the trigeneration system. A comprehensive thermoeconomic analysis and multi-objective optimization of the new trigeneration system are performed. First, a detailed parametric study is carried out to investigate the effects of key design parameters, including turbine inlet pressure, condenser temperature, basic solution ammonia concentration, air mass flow rate and heat source temperature, on the thermoeconomic criteria. Then, a multi-objective optimization is conducted to determine the best design parameters, considering exergy and total cost rate as the objective functions. The optimal solution Pareto frontier indicates that the exergy efficiency and total cost rate vary in the range of 14.9–41.6% and 1.13–2.19 $/h, respectively. Analyses of the scattered distributions of design parameters reveal that lower heat source temperatures tend to optimize the objective functions. However, altering other design parameters has a significant effect on the trade-off between exergy efficiency and total cost rate.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".