Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: A review

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  • Hao nan Guo, CAS - Institute of Geographical Sciences and Natural Resources Research, University of Chinese Academy of Sciences
  • ,
  • Shu biao Wu
  • Ying jie Tian, Chinese Academy of Sciences
  • ,
  • Jun Zhang, Guilin University of Technology
  • ,
  • Hong tao Liu, CAS - Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences

Conventional treatment and recycling methods of organic solid waste contain inherent flaws, such as low efficiency, low accuracy, high cost, and potential environmental risks. In the past decade, machine learning has gradually attracted increasing attention in solving the complex problems of organic solid waste treatment. Although significant research has been carried out, there is a lack of a systematic review of the research findings in this field. This study sorts the research studies published between 2003 and 2020, summarizes the specific application fields, characteristics, and suitability of different machine learning models, and discusses the relevant application limitations and future prospects. It can be concluded that studies mostly focused on municipal solid waste management, followed by anaerobic digestion, thermal treatment, composting, and landfill. The most widely used model is the artificial neural network, which has been successfully applied to various complicated non-linear organic solid waste related problems.

Original languageEnglish
Article number124114
JournalBioresource Technology
Volume319
Number of pages13
ISSN0960-8524
DOIs
Publication statusPublished - Jan 2021

    Research areas

  • Machine learning, Modeling, Organic solid waste, Prediction, LIGNOCELLULOSIC BIOMASS, ARTIFICIAL NEURAL-NETWORK, LEAST-SQUARES, HIGHER HEATING VALUE, METHANE EMISSIONS, ANAEROBIC CO-DIGESTION, PROCESS PARAMETERS, CLASSIFICATION-SYSTEM, SUPPORT VECTOR MACHINE, BIOGAS PRODUCTION

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