Exergy approach for advancing sustainability of a biomass boiler
Bibliographic record
Abstract
An exergy analysis of the district energy plant at University of Idaho, Moscow, Idaho, USA is presented. Exergy flows through the components of the steam cycle through the biomass boiler are quantified to identify major sources of exergy destruction. A mathematical model is developed to determine sources of exergy destruction using measurements taken. The largest sources of exergy destruction are the boiler and furnace at 35% and 33% of the overall exergy losses, respectively, followed by the campus heating equipment at 5.7% and pressure reducing valve (PRV) at 3.5%. Parametric studies reveal that decreasing boiler steam pressure levels to reduce exergy destruction in the PRV results in increased exergy destruction rates in the boiler. Increasing boiler steam pressure levels instead reduces exergy destruction, but has negligible effects on the overall exergy efficiency of the complete cycle. This indicates that the PRV is limiting potential improvements in the boiler exergy efficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".