Plastic Recycling Support Using Surface Texture for Additive Blending
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Solution Overview
Problem
The challenge in recycling plastics lies in determining the optimal blending of additives for recycled plastics with unknown physical properties and deterioration levels, as the use history of waste plastics is often unclear, making it difficult to achieve desired physical properties.
Innovation Solution
A plastic recycling supporting apparatus and method that estimates the physical properties and deterioration degree of waste plastics using texture structural features from surface analysis data, and inversely estimates the blending conditions for additives to achieve desired properties through machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If waste plastic with unknown use history is recycled, then the recycling rate is improved, but the physical property control becomes difficult
Solution Approach 1:
The system performs preliminary surface analysis and texture feature extraction from waste plastic before recycling. By pre-characterizing the plastic's surface properties and deterioration state, the system can predict the optimal additive blending ratios in advance, enabling precise physical property control even for waste plastic with unknown use history.
Solution Approach 2:
The invention introduces texture structural features extracted from surface analysis data as an intermediary parameter. These features serve as a bridge between the unknown state of waste plastic and the desired physical properties of recycled plastic, allowing the AI model to determine optimal additive blending without knowing the plastic's use history.
2Manufacturing precision
If additive blending is optimized based on known plastic properties, then the desired physical property is achieved, but the method is unclear for waste plastic with unknown deterioration degree
Solution Approach 1:
The invention replaces traditional mechanical/chemical testing methods for determining plastic deterioration with a non-contact surface analysis system. By using optical or imaging-based surface analysis to extract texture features, the system can assess the deterioration state without physical contact or complex laboratory equipment, enabling property determination for waste plastic with unknown history.
Solution Approach 2:
The system changes the assessment parameters from traditional physical/chemical property measurements to surface texture structural features. By analyzing surface texture patterns, the AI model can infer the deterioration degree and determine appropriate additive blending ratios, transforming the problem from direct property measurement to indirect feature-based prediction.
3Measurement precision
If surface analysis and AI estimation are used, then the additive blending accuracy is improved, but the device complexity increases
Solution Approach 1:
The invention extracts only the essential texture structural features from surface analysis data that are most relevant for predicting plastic properties and deterioration. By selecting and extracting only the critical features rather than processing all possible surface analysis data, the system maintains high estimation accuracy while reducing computational complexity and device requirements.
Data Source
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AI summary
Even in a case of a waste plastic whose use history is unknown, blending of an additive for recycling into a recycled plastic having a desired physical property can be estimated with high accuracy. A plastic recycling supporting apparatus 100 that supports plastic recycling in which a plastic is blended with an additive and is recycled into a recycled plastic having a desired physical property includes: a physical property and deterioration estimator 140 configured to estimate, using a physical property and deterioration estimation model, a physical property and a deterioration degree of the plastic based on a texture structural feature extracted from surface analysis data of the plastic; and a blending estimator 150 configured to estimate a physical property of the recycled plastic based on the physical property and the deterioration degree of the plastic and a blending condition of the additive using a physical property recovery model.