Introducing exergy analysis in life cycle assessment: A case study
Bibliographic record
Abstract
Life Cycle Assessment (LCA) is a methodology for assessing the potential environmental aspects associated with a product or service along its life cycle. However, in the case of energy technologies, it is suggested that the LCA of a product encompasses also further aspects other than environmental aspects and primary energy calculations. In particular, to optimize the reduction of raw materials during the whole life cycle, it is important to introduce the assessment of the irreversibility, applying the exergy analysis. In this paper, an integrated approach of exergy analysis and LCA is proposed, developing the Life-cycle quality index able to suggest potential exergy inefficiencies and the Life Cycle irreversibility index that helps the comparison of processes and products having the same functional unit. In addition, the paper introduces a new dimensionless index, the Technology Obsolescence index, to quantify the technological obsolescence of the energy system examined, merging the energy performance and the material, used both with the same units to achieve a design optimization. The indices proposed are applied to the whole life cycle of a biomass boiler. The results identify that hotspots can be traced in the use stage of the real biomass boiler, where the potential recoverable exergy has an incidence of 17.4% on the total exergy destroyed. Also, in the manufacturing stage, the cooking process produces the highest irreversibilities of the production stage.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".