Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.
Aarhus Institute of Advanced Studies, Aarhus University, DK-8000 Aarhus C, Denmark.
Bioresour Technol. 2021 Jan;319:124114. doi: 10.1016/j.biortech.2020.124114. Epub 2020 Sep 11.
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.
有机固体废物的传统处理和回收方法存在固有缺陷，如效率低、精度低、成本高和潜在的环境风险。在过去十年中，机器学习在解决有机固体废物处理这一复杂问题方面逐渐引起了越来越多的关注。尽管已经开展了大量的研究，但该领域的研究结果缺乏系统的综述。本研究对 2003 年至 2020 年期间发表的研究进行了分类，总结了不同机器学习模型的具体应用领域、特点和适用性，并讨论了相关的应用局限性和未来前景。可以得出结论，研究主要集中在城市固体废物管理上，其次是厌氧消化、热处理、堆肥和垃圾填埋。应用最广泛的模型是人工神经网络，它已成功应用于各种复杂的非线性有机固体废物相关问题。
