Artificial Intelligence in the Sorting of Municipal Waste as an Enabler of the Circular Economy

Wilts, Henning; Garcia, Beatriz Riesco; Garlito, Rebeca Guerra; Gómez, Laura Saralegui; Prieto, Elisabet González

doi:10.3390/resources10040028

Cited by 114 publications

(59 citation statements)

References 3 publications

(3 reference statements)

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“…This robot with AI could be applied with a recognition ability of more than 95%. On the other hand, textile waste recovery was not achieved, with only 13% of textile waste being recovered due to the complicated structure of materials [76]. Xiong designed a cheap near-infrared device to sort the seven types of plastics using machine learning.…”

Section: Sorting and Identification Of Textile Wastementioning

confidence: 99%

Possibility Routes for Textile Recycling Technology

et al. 2021

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The fashion industry contributes to a significant environmental issue due to the increasing production and needs of the industry. The proactive efforts toward developing a more sustainable process via textile recycling has become the preferable solution. This urgent and important need to develop cheap and efficient recycling methods for textile waste has led to the research community’s development of various recycling methods. The textile waste recycling process can be categorized into chemical and mechanical recycling methods. This paper provides an overview of the state of the art regarding different types of textile recycling technologies along with their current challenges and limitations. The critical parameters determining recycling performance are summarized and discussed and focus on the current challenges in mechanical and chemical recycling (pyrolysis, enzymatic hydrolysis, hydrothermal, ammonolysis, and glycolysis). Textile waste has been demonstrated to be re-spun into yarn (re-woven or knitted) by spinning carded yarn and mixed shoddy through mechanical recycling. On the other hand, it is difficult to recycle some textiles by means of enzymatic hydrolysis; high product yield has been shown under mild temperatures. Furthermore, the emergence of existing technology such as the internet of things (IoT) being implemented to enable efficient textile waste sorting and identification is also discussed. Moreover, we provide an outlook as to upcoming technological developments that will contribute to facilitating the circular economy, allowing for a more sustainable textile recycling process.

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Section: Sorting and Identification Of Textile Wastementioning

confidence: 99%

Possibility Routes for Textile Recycling Technology

et al. 2021

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“…A CE is a systemic procedure that contributes to maintain the activities related to reverse flows in supply chains including resource usage, recovery, recycling, reuse, repair, remanufacturing, and refurbishment [90]. It motivates the perpetual reuse of materials to reduce waste, as well as directing demand toward natural resource consumption As a recent study, Wilts et al [91] investigated the application of AI in sorting of municipal solid waste (MSW) according to the CE principles. ML was discussed as one of the most efficient technologies for automatic waste sorting.…”

Section: Application Of Machine Learning Algorithms In Circularmentioning

confidence: 99%

Application of Machine Learning in Supply Chain Management: A Comprehensive Overview of the Main Areas

Tirkolaee

Sadeghi

Mooseloo

et al. 2021

Mathematical Problems in Engineering

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In today’s complex and ever-changing world, concerns about the lack of enough data have been replaced by concerns about too much data for supply chain management (SCM). The volume of data generated from all parts of the supply chain has changed the nature of SCM analysis. By increasing the volume of data, the efficiency and effectiveness of the traditional methods have decreased. Limitations of these methods in analyzing and interpreting a large amount of data have led scholars to generate some methods that have high capability to analyze and interpret big data. Therefore, the main purpose of this paper is to identify the applications of machine learning (ML) in SCM as one of the most well-known artificial intelligence (AI) techniques. By developing a conceptual framework, this paper identifies the contributions of ML techniques in selecting and segmenting suppliers, predicting supply chain risks, and estimating demand and sales, production, inventory management, transportation and distribution, sustainable development (SD), and circular economy (CE). Finally, the implications of the study on the main limitations and challenges are discussed, and then managerial insights and future research directions are given.

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“…CE approaches for the textile industry [9], the design of new generations of yarns [10], fabrics, and garments, using regenerated wool and sustainable materials, the design of textile products improving health and wellness [11], the development of ecofriendly/sustainable processes for fabric manufacturing [12], and the ideation of smart and bio-materials are just some of the keywords for the future of textiles. Such research fields could also embrace recent trends in artificial intelligence methods for the purpose of modelling and simulating circular textile processes [13], for sorting or selecting raw materials [14], for traceability of the textile and clothing supply chain [15], and many more.…”

Section: Future Researchmentioning

confidence: 99%

Special Issue on Innovative Textiles in the Era of Circular Economy

Furferi

2021

Applied Sciences

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This work presents the Special Issue on Innovative Textiles in the Era of Circular Economy, published in the Applied Sciences Journal. Such an issue was introduced to promote papers related to the textile field aiming at the development of a range of sustainable processes, technologies, products, and actions for the improvement of human well-being and social equity. Works proposed in this Special Issue are aimed at significantly reducing environmental risks and ecological shortcomings related to the development of textile products.

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Artificial Intelligence in the Sorting of Municipal Waste as an Enabler of the Circular Economy

Cited by 114 publications

References 3 publications

Possibility Routes for Textile Recycling Technology

Possibility Routes for Textile Recycling Technology

Application of Machine Learning in Supply Chain Management: A Comprehensive Overview of the Main Areas

Special Issue on Innovative Textiles in the Era of Circular Economy

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