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Applying machine learning approach in recycling

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Abstract

Waste generation has been increasing drastically based on the world’s population and economic growth. This has significantly affected human health, natural life, and ecology. The utilization of limited natural resources, and the harming of the earth in the process of mineral extraction, and waste management have far exceeded limits. The recycling rate are continuously increasing; however, assessments show that humans will be creating more waste than ever before. Some difficulties during recycling include the significant expense involved during the separation of recyclable waste from non-disposable waste. Machine learning is the utilization of artificial intelligence (AI) that provides a framework to take as a structural improvement of the fact without being programmed. Machine learning concentrates on the advancement of programs that can obtain the information and use it to learn to make future decisions. The classification and separation of materials in a mixed recycling application in machine learning is a division of AI that is playing an important role for better separation of complex waste. The primary purpose of this study is to analyze AI by focusing on machine learning algorithms used in recycling systems. This study is a compilation of the most recent developments in machine learning used in recycling industries.

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adapted from Dhulekar et al. [61]

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adapted from Torres et al. [87]

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adapted from Deng et al. [97]

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Authors and Affiliations

  1. Department of Electrical Electronic Engineering, Iskenderun Technical University, 31200, Iskenderun, Hatay, Turkey

    Merve Erkinay Ozdemir

  2. Department of Mechanical Engineering, Wichita State University, 1845 Fairmount Street, Wichita, KS, 67260, USA

    Zaara Ali, Balakrishnan Subeshan & Eylem Asmatulu

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  1. Merve Erkinay Ozdemir
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Erkinay Ozdemir, M., Ali, Z., Subeshan, B. et al. Applying machine learning approach in recycling. J Mater Cycles Waste Manag 23, 855–871 (2021). https://doi.org/10.1007/s10163-021-01182-y

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