Sorting Mixed Plastic Waste by Density and Optical Reflection
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Solution Overview
Problem
Current methods for sorting mixed plastic packaging waste, such as KR102038807B1 and EP2384873, fail to distinguish between different types of plastics like PET, PP, PE, and PS based on color, leading to inefficiencies in recycling.
Innovation Solution
The method involves further separating PET plastic waste by structure and color, using air classifiers and optical sorters to achieve higher purity fractions, and adjusting the sequence of sorting PET trays and colors to optimize recycling efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If plastic packaging waste is sorted only by material type without distinction of color and structure, then the sorting process is simpler and faster, but the purity and quality of sorted fractions is insufficient for specific recycling applications
Solution Approach 1:
The sorting process is divided into multiple sequential stages: initial material type separation, followed by structure-based separation (trays vs. bottles), and finally color-based separation. Each stage uses appropriate sorting technology tailored to the specific differentiation need, progressively refining the waste streams to achieve high-purity fractions suitable for specific recycling applications.
Solution Approach 2:
Material type separation is performed as a preliminary step before structure and color differentiation. This preliminary sorting reduces the complexity of subsequent sorting stages by narrowing down the waste stream to specific polymer types, making the overall sorting process more efficient and manageable.
2Manufacturing precision
If PET plastic waste is sorted by structure and color using multiple optical sorters, then the purity of sorted fractions is improved, but the sorting time and process complexity increase
Solution Approach 1:
The PET sorting process is segmented into distinct functional stages: structure detection and separation (trays vs. bottles), followed by color detection and separation. Each stage uses optimized optical sorting parameters and sequential processing to achieve high-purity fractions while minimizing redundant sorting operations.
Solution Approach 2:
Different sorting criteria and optical detection parameters are applied locally to different PET waste streams based on their specific characteristics. Structure-based sorting uses geometric and dimensional analysis, while color-based sorting uses spectral analysis, allowing each sorting stage to be optimized for its specific differentiation task.
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
This approach enables targeted sorting and recycling of PET plastic waste, improving the purity and quality of sorted fractions by distinguishing between different colors and structures, which is crucial for specific applications.
Implementation Method 1
The plastic packaging waste is sorted on the basis of material type and on the basis of color by means of optical sorters
Implementation Method 2
a first step of sorting the mixed plastic packaging waste into a lower-density fraction, comprising films, and a higher-density fraction
Data Source
Figure 1
AI summary
The present invention relates to a method for sorting mixed plastic packaging waste comprising PE, PET, PP, PS, MPO and films, wherein the mixed plastic packaging waste is first sorted by means of air classifiers, wherein the air classifiers sort the mixed plastic packaging waste into a lower-density fraction, comprising films, and a higher-density fraction, wherein the higher-density fraction is sorted on the basis of differences in reflection of electromagnetic radiation, resulting in a PE, PET, PP, PS and MPO fraction and residual mixed plastic packaging waste, wherein the PET fraction, comprising PET trays and colored PET, is sorted on PET trays by means of an optical sorter, wherein the PET fraction is then further sorted by color.