Comparative energy, exergy, economic, and environmental (4E) analysis and optimization of two high-temperature Kalina cycles integrated with thermoelectric generators for waste heat recovery from a diesel engine
Bibliographic record
Abstract
High-temperature Kalina cycles are among the efficient approaches to recovering waste heat. However, high-temperature Kalina cycles are characterized by complex layouts, lower efficiency, and higher cost. This work aims at conducting a comparative thermodynamic, economic, and environmental assessment of two different configurations of the high-temperature Kalina cycle. Thermoelectric generators, as the simple and developing heat recovery modules for electricity generation, have been embedded in the condensers of the simple High-temperature Kalina cycle, thereby proposing the enhanced thermal performance of the Kalina cycles. A parametric optimization approach was adopted to optimize the net power output and the location of pinch points of condensers. The results indicate that thermoelectric generators can improve the total power capacity of High-temperature Kalina cycles by about 0.29–0.82 kW. Meanwhile, the economic feasibility of this integration has also been investigated. The first enhanced Kalina cycle has energy and exergy efficiencies of 32 % and 63.23 %, respectively. As an economic parameter, the net present values of these cycles are 84.56, 84.77, 86.63, and 86.84 k$ for standard Kalina cycle configuration 1, enhanced Kalina cycle configuration 1, standard Kalina cycle configuration 2, and enhanced Kalina cycle configuration 2, respectively. In addition, the environmental assessment reveals that 15.55, 15.85, 15.54, and 15.84 lit/hr diesel fuel would be saved by operating the waste heat recovery cycles, respectively. Finally, a parametric study has been carried out to study the influences of variations of different parameters on the performance criteria of all four cycles.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".