Wind Turbine Rotor Blade Quality Grading for Composite Reuse
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Solution Overview
Problem
Efficient and effective methods for recycling wind turbine components, particularly those made of composite materials, are lacking, leading to materials being discarded in landfills due to the inability to separate and assess their quality for reuse in new products.
Innovation Solution
A method using machine learning to classify materials of a wind turbine rotor blade by determining quality grades based on historical operational data, end-of-life testing, and data modeling to provide recycling recommendations, including cutting and bin placement instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If composite materials are recycled without quality assessment, then recycling process is simple, but material quality cannot be guaranteed for safety-critical applications
Solution Approach 1:
The rotor blade is segmented into multiple sections along its length, with each section being independently evaluated for material quality. This allows different portions of the same blade to be assigned different quality grades based on their specific characteristics, enabling both simplified recycling processing and reliable material assessment for safety-critical applications.
Solution Approach 2:
Different sections of the rotor blade are assigned different quality grades based on their specific material conditions, damage history, and operational characteristics. This local quality assessment allows high-quality sections to be used in safety-critical applications while lower-quality sections are appropriately allocated to non-critical uses, resolving the contradiction between simple recycling and reliable quality assurance.
2Device complexity
If all materials are treated as equal quality, then sorting and assessment processes are avoided, but higher-quality materials cannot be reused in appropriate products
Solution Approach 1:
The system evaluates different sections of the rotor blade individually and assigns quality grades based on their specific characteristics. This allows the blade to be sorted into multiple quality categories (e.g., first quality grade, second quality grade) without requiring complex assessment of every possible material variation, thus avoiding excessive complexity while enabling flexible reuse of higher-quality materials in appropriate products.
Solution Approach 2:
The quality assessment and grading of rotor blade sections is performed before the recycling process begins. By pre-evaluating and categorizing materials based on their quality, the system avoids the need for complex real-time sorting and assessment during recycling operations, reducing overall device complexity while maintaining the ability to adapt materials to different product requirements.
3Measurement precision
If historical operational data is collected and analyzed, then material quality can be accurately assessed, but data processing and modeling resources are required
Solution Approach 1:
The data processing system is segmented into modular components that handle different aspects of quality assessment independently. Historical operational data is processed in discrete sections, with each section contributing to the overall quality grade determination. This segmentation reduces the complexity of the complete data processing system while maintaining accurate quality assessment through integrated analysis of multiple data sources.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate assessment of material quality for recycling, allowing higher-grade materials to be reused in appropriate products, thereby reducing landfill waste and increasing the value of recycled materials.
Implementation Method 1
separating each material of a composite material by exposing the composite material to a swelling agent (e.g., formic acid, etc.) for a certain time period. This causes the epoxy resin to disintegrate, allowing each material that had been bound together using the epoxy resin to separate.
Data Source
AI summary
A device may receive historical operational data for a mechanical system, such as a rotor blade of a wind turbine. The device may determine one or more quality grades for each of one or more materials of the system, e.g., the rotor blade. The one or more quality grades may be determined by using a data model to process the historical operational data. The data model may be trained using machine learning based on one or both of historical operational data for similar systems, e.g., other rotor blades, and end-of-life (EOL) testing data for the same. The device may determine a recycling recommendation based on the one or more quality grades. The recycling recommendation may include instructions relating to recycling the one or more materials. The device may deliver the recycling recommendation to another device or recipient.