Thermodynamic Analysis of a Power Plant Integrated with Fogging Inlet Cooling and a Biomass Gasification
Bibliographic record
Abstract
Biomass energy and especially biofuels produced by biomass gasification are clean and renewable options for power plants. Also, on hot days the performance of gas turbines decreases substantially, a problem that can be mitigated by fog cooling. In the present paper, a biomass-integrated fogging steam injected gas turbine cycle is analyzed with energy and exergy methods. It is observed that (1) increasing the compressor pressure ratio raises the air flow rate in the plant but reduces the biomass flow rate; (2) increasing the gas turbine inlet temperature decreases the air and biomass flow rates; (3) increasing the compressor pressure ratio raises the energy and exergy efficiencies, especially at lower pressure ratios; (4) increasing the gas turbine inlet temperature raises both efficiencies; and (5) overspray increases the energy efficiency and net cycle power slightly. The gas turbine exhibits the highest exergy efficiency of the cycle components and the combustor the lowest. A comparison of the cycle with similar cycles fired by natural gas and differently configured cycles fueled by biomass shows that the cycle with natural gas firing has an energy efficiency 18 percentage points above the biomass fired cycle, and that steam injection increases the energy efficiency about five percentage points relative to the cycle without steam injection. Also, the influence of steam injection on energy efficiency is more significant than fog cooling.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".