Machine learning for recycling analytics is proving to be a practical investment, with breakthroughs suggesting its potential as an industry standard. Complex waste streams complicate recycling efforts, requiring better sorting methods for effectiveness at scale. Accurate data-driven strategies can enhance sorting and disassembly processes, with real-time insights being particularly beneficial. Innovative uses of computer vision technology, such as TFC Recycling's AI-based system for detecting paper, exemplify the improvements possible through machine learning in recycling operations. This technology allows for precise sorting and operational efficiencies in recycling programs.
TFC Recycling recently developed AI-based computer vision technology to detect paper, which was made possible by a grant from the Foodservice Packaging Institute. It is among the largest recycling companies in Virginia, servicing over 500,000 households in several cities.
Analytics provides a solution. Data informs actionable strategies that could drastically improve sorting and disassembly. Real-time insights are ideal because reports can quickly become outdated.
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