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

Du Ni, Zhi Xiao, Ming Lim

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)(In-Press)
JournalSoft Computing
Volume(In-Press)
Early online date25 Jan 2021
DOIs
Publication statusE-pub ahead of print - 25 Jan 2021

Funder

National Natural Science Foundation of China (Grant Nos. 71671019, 72071021, and 71871034).

Keywords

  • Algorithms
  • Literature review
  • Machine learning
  • Recycling

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Geometry and Topology

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