Applying machine learning approach in recycling

Ozdemir, Merve Erkinay; Ali, Zulfiqar; Subeshan, Balakrishnan; Asmatulu, Eylem

doi:10.1007/s10163-021-01182-y

Cited by 70 publications

(18 citation statements)

References 119 publications

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“…Waste disposal methods can be categorized into four main types: waste recycling, waste incineration, waste composting, and waste landfilling (Chen 2022 ). Waste recycling involves collecting, treating, and reusing human-generated waste (Erkinay Ozdemir et al 2021 ). Waste incineration uses high-temperature and high-pressure pyrolysis oxidation to reduce the volume of waste and eliminate hazardous materials (Chen et al 2022a ).…”

Section: Illegal Dumping and Waste Disposalmentioning

confidence: 99%

Artificial intelligence for waste management in smart cities: a review

et al. 2023

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The rising amount of waste generated worldwide is inducing issues of pollution, waste management, and recycling, calling for new strategies to improve the waste ecosystem, such as the use of artificial intelligence. Here, we review the application of artificial intelligence in waste-to-energy, smart bins, waste-sorting robots, waste generation models, waste monitoring and tracking, plastic pyrolysis, distinguishing fossil and modern materials, logistics, disposal, illegal dumping, resource recovery, smart cities, process efficiency, cost savings, and improving public health. Using artificial intelligence in waste logistics can reduce transportation distance by up to 36.8%, cost savings by up to 13.35%, and time savings by up to 28.22%. Artificial intelligence allows for identifying and sorting waste with an accuracy ranging from 72.8 to 99.95%. Artificial intelligence combined with chemical analysis improves waste pyrolysis, carbon emission estimation, and energy conversion. We also explain how efficiency can be increased and costs can be reduced by artificial intelligence in waste management systems for smart cities.

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Section: Illegal Dumping and Waste Disposalmentioning

confidence: 99%

Artificial intelligence for waste management in smart cities: a review

et al. 2023

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“…Exported plastic waste (10%) was included, as the European Commission has stated that all PPW should be recycled in the EU market by 2030 after the Chinese government’s ban (April 2017) on imported recyclable solid waste [ 191 ]. An improvement in the amount of recycled plastic should be derived from improved sorting and preprocessing to reach higher purity of PCPW fractions at RECs; this is achievable by coupling traditional sorting technologies (i.e., manual sorting and NIR sensors) with machine-learning-based recognition of plastic waste from images [ 192 , 193 ]. This is expected to only increase the recycling rate in RECs, while thermoplastics rejects generated by mixed and small-particle plastics would remain an issue.…”

Section: Perspectivesmentioning

confidence: 99%

Microbial Enzyme Biotechnology to Reach Plastic Waste Circularity: Current Status, Problems and Perspectives

Orlando

Molla

Castellani

et al. 2023

IJMS

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The accumulation of synthetic plastic waste in the environment has become a global concern. Microbial enzymes (purified or as whole-cell biocatalysts) represent emerging biotechnological tools for waste circularity; they can depolymerize materials into reusable building blocks, but their contribution must be considered within the context of present waste management practices. This review reports on the prospective of biotechnological tools for plastic bio-recycling within the framework of plastic waste management in Europe. Available biotechnology tools can support polyethylene terephthalate (PET) recycling. However, PET represents only ≈7% of unrecycled plastic waste. Polyurethanes, the principal unrecycled waste fraction, together with other thermosets and more recalcitrant thermoplastics (e.g., polyolefins) are the next plausible target for enzyme-based depolymerization, even if this process is currently effective only on ideal polyester-based polymers. To extend the contribution of biotechnology to plastic circularity, optimization of collection and sorting systems should be considered to feed chemoenzymatic technologies for the treatment of more recalcitrant and mixed polymers. In addition, new bio-based technologies with a lower environmental impact in comparison with the present approaches should be developed to depolymerize (available or new) plastic materials, that should be designed for the required durability and for being susceptible to the action of enzymes.

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“…However, unstandardized medical waste sorting has led to the erroneous disposal of items as infectious waste [ 59 ], imposing unnecessary costs associated with infectious waste treatment [ 4 ]. This challenge might be addressed using machine learning algorithms, as they have been utilized to sort and classify wastes for different industrial applications, such as plastic waste, bottle, and municipal solid waste recycling, with >90% accuracy [ 60 ]. Training healthcare workers on waste management may improve their practices in biomedical waste handling and disposal [ 61 , 62 ].…”

Section: Challenges and Outlook In Recycling And Reusing Medical Wastementioning

confidence: 99%