Sustainable approach to integrate a conventional power plant and renewable energies, coupled with the Goswami cycle and Maisotsenko heat exchanger
Bibliographic record
Abstract
Abstract This paper presents a pioneering investigation into an integrated power generation cycle that combines gas turbines, steam turbines, Goswami cycles, biomass fuel, solar energy sources, and advanced Maisotsenko heat exchangers. Biomass gasification has been applied to partially fuel the gas turbine, while the M‐Cycle serves as the gas turbine inlet air cooling system. Solar parabolic collectors are utilized to elevate the compressed air temperature for combustion, and the Goswami cycle generates both power and cooling through waste heat recovery. This research addresses a critical gap by undertaking a holistic examination of the integrated power generation cycle, modelling the overall system performance by considering interactions and synergies between various components. The base model has been implemented, and the impacts of influential parameters on system efficiency, such as ambient temperature, seasonal variation, and steam‐to‐biomass ratio, have been taken into consideration. Remarkably, the incorporation of the M‐Cycle, solar parabolic systems, steam cycles, and the Goswami cycle into the base gas cycle results in substantial energy efficiency enhancements of 1.59%, 1.96%, 16.87%, and 6.06%, respectively, highlighting the transformative potential of this integrated approach for sustainable and efficient power generation.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".