Artificial intelligence for waste management in smart cities: a review

Environmental Chemistry Letters, Journal Year: 2023, Volume and Issue: 21(4), P. 1959 - 1989

Published: May 9, 2023

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

Language: Английский

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Forecasting plastic waste generation and interventions for environmental hazard mitigation

Journal of Hazardous Materials, Journal Year: 2021, Volume and Issue: 424, P. 127330 - 127330

Published: Sept. 23, 2021

Language: Английский

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Applications of convolutional neural networks for intelligent waste identification and recycling: A review

Resources Conservation and Recycling, Journal Year: 2022, Volume and Issue: 190, P. 106813 - 106813

Published: Dec. 14, 2022

Language: Английский

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The Critical Role of Process Analysis in Chemical Recycling and Upcycling of Waste Plastics

Annual Review of Chemical and Biomolecular Engineering, Journal Year: 2022, Volume and Issue: 13(1), P. 301 - 324

Published: March 23, 2022

There is an urgent need for new technologies to enable circularity synthetic polymers, spurred by the accumulation of waste plastics in landfills and environment contributions manufacturing climate change. Chemical recycling a promising means convert into molecular intermediates that can be remanufactured products. Given growing interest development chemical approaches, it critical evaluate economics, energy use, greenhouse gas emissions, other life cycle inventory metrics emerging processes,relative incumbent, linear practices employed today. Here we offer specific definitions classes upcycling describe general process concepts mixed waste. We present framework techno-economic analysis assessment both closed- open-loop recycling. Rigorous application these tools will required impactful solutions problem.

Language: Английский

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Smart waste management: A paradigm shift enabled by artificial intelligence

Waste Management Bulletin, Journal Year: 2024, Volume and Issue: 2(2), P. 244 - 263

Published: May 9, 2024

Waste management poses a pressing global challenge, necessitating innovative solutions for resource optimization and sustainability. Traditional practices often prove insufficient in addressing the escalating volume of waste its environmental impact. However, advent Artificial Intelligence (AI) technologies offers promising avenues tackling complexities systems. This review provides comprehensive examination AI's role management, encompassing collection, sorting, recycling, monitoring. It delineates potential benefits challenges associated with each application while emphasizing imperative improved data quality, privacy measures, cost-effectiveness, ethical considerations. Furthermore, future prospects AI integration Internet Things (IoT), advancements machine learning, importance collaborative frameworks policy initiatives were discussed. In conclusion, holds significant promise enhancing practices, such as concerns, cost implications is paramount. Through concerted efforts ongoing research endeavors, transformative can be fully harnessed to drive sustainable efficient practices.

Language: Английский

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Revolutionizing municipal solid waste management (MSWM) with machine learning as a clean resource: Opportunities, challenges and solutions

Fuel, Journal Year: 2023, Volume and Issue: 348, P. 128548 - 128548

Published: May 4, 2023

Language: Английский

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

International Journal of Molecular Sciences, Journal Year: 2023, Volume and Issue: 24(4), P. 3877 - 3877

Published: Feb. 15, 2023

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

Language: Английский

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Recent Developments in Technology for Sorting Plastic for Recycling: The Emergence of Artificial Intelligence and the Rise of the Robots

Recycling, Journal Year: 2024, Volume and Issue: 9(4), P. 59 - 59

Published: July 15, 2024

Plastics recycling is an important component of the circular economy. In mechanical recycling, recovery high-quality plastics for subsequent reprocessing requires plastic waste to be first sorted by type, color, and size. chemical certain types should removed as they negatively affect process. Such sortation objects at Materials Recovery Facilities (MRFs) relies increasingly on automated technology. Critical any sorting proper identification type. Spectroscopy used this end, augmented machine learning (ML) artificial intelligence (AI). Recent developments in application ML/AI are highlighted here, state art presented. Commercial equipment recyclables identified from a survey publicly available information. Automated equipment, ML/AI-based sorters, robotic sorters currently market evaluated regarding their sensors, capability sort plastics, primary application, throughput, accuracy. This information reflects rapid progress achieved plastics. However, film, dark comprising multiple polymers remains challenging. Improvements and/or new solutions forthcoming.

Language: Английский

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Machine learning applications for electrospun nanofibers: a review

Journal of Materials Science, Journal Year: 2024, Volume and Issue: 59(31), P. 14095 - 14140

Published: July 30, 2024

Abstract Electrospun nanofibers have gained prominence as a versatile material, with applications spanning tissue engineering, drug delivery, energy storage, filtration, sensors, and textiles. Their unique properties, including high surface area, permeability, tunable porosity, low basic weight, mechanical flexibility, alongside adjustable fiber diameter distribution modifiable wettability, make them highly desirable across diverse fields. However, optimizing the properties of electrospun to meet specific requirements has proven be challenging endeavor. The electrospinning process is inherently complex influenced by numerous variables, applied voltage, polymer concentration, solution flow rate, molecular weight polymer, needle-to-collector distance. This complexity often results in variations nanofibers, making it difficult achieve desired characteristics consistently. Traditional trial-and-error approaches parameter optimization been time-consuming costly, they lack precision necessary address these challenges effectively. In recent years, convergence materials science machine learning (ML) offered transformative approach electrospinning. By harnessing power ML algorithms, scientists researchers can navigate intricate space more efficiently, bypassing need for extensive experimentation. holds potential significantly reduce time resources invested producing wide range applications. Herein, we provide an in-depth analysis current work that leverages obtain target nanofibers. examining work, explore intersection ML, shedding light on advancements, challenges, future directions. comprehensive not only highlights processes but also provides valuable insights into evolving landscape, paving way innovative precisely engineered various Graphical abstract

Language: Английский

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Applying machine learning approach in recycling

Journal of Material Cycles and Waste Management, Journal Year: 2021, Volume and Issue: 23(3), P. 855 - 871

Published: Feb. 17, 2021

Language: Английский

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