Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: A review - Guo, Hao-nan; Wu, Shu-biao; Tian, Ying-jie; Zhang, Jun; Liu, Hong-tao (Bioresource technology, 2021)
[Display omitted] •Five specific application fields of ML in OSW were identified and analyzed.•Characteristics and suitability of different ML models were summarized.•Most frequently employed ML model is ANN, followed by SVM, GA, and DT/RF.•Data scarcity hinders implementation of ML in OSW treatment and recycling.•Low interpretability and unclear selection basis of ML models need to be overcome. 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.