Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: A review.
By Hao-nan Guo, Shu-biao Wu, Yingtao Tian, Jun Zhang, Hong-tao Liu. Bioresource Technology, 2020
310 citations recorded by the source corpus.
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.
Subfields: Food Science, Industrial and Manufacturing Engineering, Public Health, Environmental and Occupational Health
View Paper: https://www.semanticscholar.org/paper/471099b9576e858fe71c4b447ba958173801e363
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
Citations in the corpus
- Artificial intelligence for waste management in smart cities: a review (2023, methodology): “By separating contaminants like plastics and stones from feedstock, compost, and solid digestate, the machine learning algorithm can be used in bioprocessing to lower possible environmental risks and enhance the profitability of composting and anaerobic digestion products (Guo et al. 2021).”