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Read original →The Geometry of the Circular Economy
How packaging geometry affects waste sorting, why AI and digital labeling boost recycling efficiency to 93%, and how mathematical optimization of waste collection routes cuts costs by 40-66%. Technologies and case studies from the circular economy.

AI summary
The circular economy is transforming the spatial architecture of waste management—from packaging design to urban logistics. Geometry is becoming a working tool: packaging design is now engineered for AI-powered sorting lines, while municipal solid waste collection routes are optimized through mathematical modeling of flow graphs. The implementation of digital technologies, mono-materials, and computer vision systems increases recycling efficiency by 10-90% and reduces logistics costs by up to 40%.
The fundamental shift in the architecture of economic activity brought about by the circular economy is increasingly taking on the contours of a distinct geometric dimension. It's not just thinking and the habits of producers and consumers that are being transformed—the spatial architecture of waste flows themselves is changing: from the micro level of packaging, forms, and tools for municipal solid waste collection to the macro level of urban arteries, rethinking ergonomics, and installing production lines along which waste moves. Geometry is no longer a metaphor or discipline—it's becoming a working tool in shaping a culture of responsible behavior among producers and consumers and justifying technical and economic proposals in interdisciplinary circular economy projects in an era of rapid artificial intelligence (AI) development.
The geometry of packaging: why shape matters
The fate of packaging on the sorting line is largely predetermined by its shape. Flat, flexible containers—film, bags, wrappers—pose the main problem for optical scanners. They're difficult to identify and even harder to separate precisely using air jets. Rigid, three-dimensional packaging—bottles, cans, trays—sorts far more efficiently. Multi-layer packaging like beverage cartons, composed of cardboard, aluminum, and plastic, has remained a stumbling block for recyclers for decades.
The response to this challenge has been the convergence of two strategies:
- Simplifying geometry and rethinking the composition of the material itself.
- Implementing digital marking that allows equipment to "see" through the form.
The "Holy Grail 2.0" project, which brought together brands and technology companies, demonstrated that digital watermarks—invisible marks applied across the entire surface of packaging—deliver sorting accuracy of approximately 90% and higher even in challenging industrial conditions, with contamination and material overlap. During trials at the Hündgen facility in Germany, detection efficiency ranged from 86.7% to 93.6%.
Sources (14)
- 1. Pioneering digital watermarks for smart packaging recycling in the EU [Electronic resource]. URL: (date of access 15.05.2026)
- 2. Digital Watermarks Initiative Holy Grail 2.0 [Electronic resource]. (date of access 15.05.2026)
- 3. Mondi’s mono-material retort pouches evidence effective sorting during recycling [Electronic resource]. URL: (date of access 17.05.2026)
- 4. Tetra Pak announces major investment to enhance sorting of food and beverage cartons in the UK [Electronic resource]. URL: (date of access 17.05.2026)
- 5. RFID reveals the true recyclability of packaging [Electronic resource]. URL: (date of access 19.05.2026)
- 6. ППК РЭО: 7 российских заводов по сортировке отходов внедрили искусственный интеллект на производстве [Электронный ресурс]. URL: (дата обращения 20.05.2026)
- 7. Башкатов Д.А., Русинов Р.А., Полулях Л.А. Подходы к применению искусственного интеллекта для сортировки твердых отходов в России // Металлургия черных, цветных и редких металлов. № 1 (151) DOI
- 8. Confederation of European Waste-to-Energy Plants: Circular Economy 2035 Waste Treatment Gap – Detailed Explanations. URL: 2019/07/CEWEP-residualwaste-calculation-explanationsfinal.pdf. (date of access 14.05.2026)
- 9. Zhang, Y., Wei, Y., Zhang, B. et al. Fuzzy optimization of municipal solid waste collection routing under uncertain emissions. Sci Rep 16, 4857 (2026)
- 10. Vilchez-Torres, M., Ramos Castillo, Nataly Lisbeth, & Bobadilla Asto, Luis Eduardo. (2023). Optimization of Solid Waste Collection Routes Using Graph Theory and Linear Program. LACCEI, 1(8)
- 11. Sedat Yalçınkaya Calculation of Solid Waste Collection Induced Air Pollutant Emissions through Spatial Analysis for Different Vehicle Capacities: A Case Study in Cigli, Izmir. Araştırma Makalesi / Research Article, Doğ Afet Çev Derg, 2020; 6(2): 366-376, DOI: 10.21324/dacd.675605.
- 12. ROSE Route optimisation module for waste logistic platforms (date of access 21.05.2026)
- 13. Mavrin, V.; Makarova, I. Developing a Decision Support System to Improve the Waste Transportation Process. Logistics 2026, 10, 78
- 14. Гвилия Н.А., Янковский Д.И. Управление логистикой отходов в мегаполисах на основе методов имитационного моделирования // Территория новых возможностей. Вестник Владивостокского государственного университета. 2025. Т. 17, № 3. С. 7–21. DOI: EDN: https://elibrary.ru/VSJBUY