Plastic Recycling Traceability via Cloud Data Structures
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Solution Overview
Problem
The challenge in plastic recycling is managing the information involved in the recycling process to satisfy all stakeholders, particularly due to the heterogeneity of incoming recyclable materials, which requires accurate tracing and quality assurance.
Innovation Solution
An apparatus and method that integrate mass balance and traceability details by checking databases for delivery and recycling process data, generating data structures for recycling processes, and providing these data structures via cloud services, ensuring accurate tracking and quality control of recyclable plastic materials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If material tracing down to delivery lot level is implemented, then quality assurance of recycled material is improved, but device complexity and data management burden increase
Solution Approach 1:
The system segments the recycling process into distinct phases (unloading, dry sorting, pelletization/compounding, packaging) with separate data structures for each phase. This segmentation allows detailed tracking at each stage without creating a monolithic complex data management system, enabling quality assurance through phase-specific monitoring while maintaining manageable data organization.
Solution Approach 2:
The data structure serves multiple functions simultaneously: it tracks material flow through all process phases, calculates mass balances, provides quality assurance documentation, and enables troubleshooting. This multi-functionality reduces the need for separate systems for each purpose, thereby improving reliability without proportionally increasing device complexity.
2Measurement precision
If mass balance calculation is performed at each process phase, then material tracking accuracy is improved, but processing time and computational load increase
Solution Approach 1:
The system performs mass balance calculations at each process phase as data becomes available, rather than waiting for complete process data. By calculating mass balances incrementally during unloading, dry sorting, and pelletization/compounding phases, the system achieves high material tracking accuracy without delaying the overall process, reducing computational wait time.
3Loss of information
If real-time data collection and cloud service provision is implemented, then traceability and troubleshooting capability are improved, but data transmission and storage requirements increase
Solution Approach 1:
The system extracts and stores only the essential data elements needed for traceability and mass balance calculation at each process phase, rather than capturing all possible process data. This selective extraction includes delivery data, process phase data, and mass balance results, providing sufficient traceability information while minimizing data storage and transmission requirements.
Data Source
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AI summary
According to an example aspect of the present invention, there is provided an apparatus configured to: check a database for delivery data relating to an incoming shipment comprising recyclable plastic material, generate, a data structure associated with a recycling process which transforms the recyclable plastic material into recycled plastic material, the recycling process comprising unloading, dry sorting, and pelletization or compounding of the recyclable plastic material, wherein the recycled plastic material is packaged into at least one shipment lot after the recycling process, and check, at predefined intervals, the database for data associated with the recycling process and add the data to the data structure, and provide the data structure via a cloud service.