Two-Objective Optimization of a Cogeneration System Based on a Gas Turbine Integrated with Solar-Assisted Rankine and Absorption Refrigeration Cycles
Bibliographic record
Abstract
The current study investigates a cogeneration system based on a gas turbine, integrated with a Rankine cycle and an absorption refrigeration cycle, considering energy and exergy perspectives. The fuel used in the gas turbine’s combustion chamber is obtained through biomass gasification, specifically using wood as the biomass fuel. To enhance the system’s performance, solar energy is utilized to preheat the working fluid in the Rankine cycle, reducing the energy required in the heat recovery steam generator. Additionally, an absorption refrigeration cycle is incorporated to recover waste heat from exhaust gases and improve the plant’s exergy efficiency. A two-objective optimization is conducted to determine the optimal operating conditions of the proposed system, considering exergy efficiency and carbon dioxide emission index as criteria. The case study reveals that the gasifier and combustion chamber contribute the most to system irreversibility, accounting for 46.7% and 22.9% of the total exergy destruction rate, respectively. A parametric study is performed to assess the impact of compression ratio, turbine bleed steam pressure, gas turbine inlet temperature, and solar share (the ratio of energy received by solar collectors to biomass fuel input energy) on system performance. The findings demonstrate that maximum energy and exergy efficiencies of the power generation system are achieved at a pressure ratio of 10. Furthermore, a 1% reduction in the gas turbine’s compression pressure ratio can be compensated by a 9.3% increase in the solar share within the steam Rankine cycle.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".