Machine learning in recycling business: an investigation of its practicality, benefits and future trends

Machine learning (ML) algorithms, such as neural networks, random forest, and more recent deep learning, are illustrating their utility for waste recycling. The increasing computational power of ML makes waste generation prediction, even at municipal level, possible with satisfying accuracy. ML is so critical and efficient and yet it is severely under-researched in recycling business. Also, the ML application in the recycling business is still a niche area judged by the limitations in its literature sources, the research domains, the ML algorithms’ use and benefits involved or reported in the literature. To unlock the value of ML in recycling business, this paper reviewed 51 related articles systematically and presents the current obstacles and future directions in applying ML to waste recycling industries

Recycling technologies

Recycling Technologies: Paper Fiber Waste Paper Characteristics Waste Paper Recycling Technologies Recycling Technologies: Glass Cullet Contaminants Cullet Recycling Technologies Recycling Technologies: Metals Ferrous Metals Ferrous Metal Recycling Technologies Nonferrous Metals Nonferrous Metal Recycling Technologies Recycling Technologies: Plastics Waste Plastic Sources and Characteristics Waste Plastic Recycling Technologies Recycling Technologies: Fibers (Textiles and Carpets) Textiles Textiles Recycling Technologies Carpets Carpet Recycling Technologies Future Directions: Innovative Control/Sorting Devices/Logics Integration in Recycling Plant