Recyclable Wind Turbine Blades: A Life Cycle Analysis
Bibliographic record
Abstract
The shift towards renewable energy has highlighted the importance of sustainable practices in wind power development, particularly concerning the end-of-life (EoL) management of wind turbine blades. Conventional blades made from thermoset resins present significant recycling challenges due to their cross-linked structure, which often leads to landfill disposal or energy-intensive recycling processes. This study evaluates the environmental impacts of 45 m wind turbine blades using the Eco Audit approach across four primary life cycle stages: material production, manufacturing, transportation, and operation and maintenance. Six blade models with different fiber and resin configurations are assessed, focusing on a comparison between conventional thermoset resins and Elium, a newly developed liquid thermoplastic resin by Arkema. Elium offers promising recyclability options, including mechanical and chemical processes, which could substantially lower the environmental burden. Compared to composites made with thermoset resins, Elium-based blades demonstrate up to a 22.5% reduction in embodied energy and a 16% decrease in carbon footprint. Additionally, Elium's compatibility with existing manufacturing processes, room-temperature curing capability, and lower processing energy contribute to its industrial feasibility. Notably, the analysis reveals that the material production phase significantly contributes to the total environmental impact, accounting for up to 98% of the embodied energy and carbon footprint in certain blade models, underscoring the importance of selecting a more sustainable resin, such as Elium, from the outset to reduce the overall environmental load.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".