Dharshika Sugumaran, Madushan D. Udakandage, Sanduni P. Kodippili, Maleesha M. De Alwis, Danushika L. Attigala, Neeliya N. Ranasinghe, Danushika C. Manatunga, Rohan S. Dassanayake, Yang Zhou, Yuanyuan Liu
{"title":"Artificial intelligence in sustainable organic waste treatment: a review","authors":"Dharshika Sugumaran, Madushan D. Udakandage, Sanduni P. Kodippili, Maleesha M. De Alwis, Danushika L. Attigala, Neeliya N. Ranasinghe, Danushika C. Manatunga, Rohan S. Dassanayake, Yang Zhou, Yuanyuan Liu","doi":"10.1007/s42768-025-00246-1","DOIUrl":null,"url":null,"abstract":"<div><p>Waste and waste generation are inevitable aspects of human life, especially organic waste, and have evolved with societal and industrial development. Waste generation cannot be entirely prevented, but it can be treated, managed, and minimized through various sustainable practices to mitigate its environmental and health impacts. Current organic waste management techniques include composting, anaerobic digestion, incineration, and hydrothermal treatment. Even though these techniques help to treat and manage organic waste, they face numerous challenges, such as the complexity of organic waste, difficulty in collection and segregation, water pollution, and greenhouse gas (GHG) emissions. Notably, there is an urgent need to reduce and control the large volume of waste generated in a short timeframe. Artificial intelligence (AI)- and machine learning (ML)-based waste management systems have recently been considered for treating organic waste due to their optimized waste collection routes, automatic sorting, efficient recovery, and contaminant reduction. In particular, AI models can facilitate and accelerate the implementation of the circular economy concept, thereby maximizing resource optimization to achieve the United Nations (UN) sustainable development goals (SDGs). The current review summarizes recently published research studies on AI-based technologies and their applications in organic waste treatment and management, including the prediction and monitoring of waste generation, automated waste collection, sorting, classification, bioconversion and treatment process optimization, waste recycling, bin-level monitoring, and vehicle routing. The major prospects and challenges of using AI technology in organic waste treatment, as well as the future directions of AI-based waste management practices, are also discussed. This review also provides exclusive coverage of various types of organic waste, conventional organic waste treatment methods and their limitations, as well as the role of organic waste management in achieving the SDGs.</p><h3>Graphical abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":807,"journal":{"name":"Waste Disposal & Sustainable Energy","volume":"7 3","pages":"539 - 560"},"PeriodicalIF":0.0000,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Waste Disposal & Sustainable Energy","FirstCategoryId":"6","ListUrlMain":"https://link.springer.com/article/10.1007/s42768-025-00246-1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Waste and waste generation are inevitable aspects of human life, especially organic waste, and have evolved with societal and industrial development. Waste generation cannot be entirely prevented, but it can be treated, managed, and minimized through various sustainable practices to mitigate its environmental and health impacts. Current organic waste management techniques include composting, anaerobic digestion, incineration, and hydrothermal treatment. Even though these techniques help to treat and manage organic waste, they face numerous challenges, such as the complexity of organic waste, difficulty in collection and segregation, water pollution, and greenhouse gas (GHG) emissions. Notably, there is an urgent need to reduce and control the large volume of waste generated in a short timeframe. Artificial intelligence (AI)- and machine learning (ML)-based waste management systems have recently been considered for treating organic waste due to their optimized waste collection routes, automatic sorting, efficient recovery, and contaminant reduction. In particular, AI models can facilitate and accelerate the implementation of the circular economy concept, thereby maximizing resource optimization to achieve the United Nations (UN) sustainable development goals (SDGs). The current review summarizes recently published research studies on AI-based technologies and their applications in organic waste treatment and management, including the prediction and monitoring of waste generation, automated waste collection, sorting, classification, bioconversion and treatment process optimization, waste recycling, bin-level monitoring, and vehicle routing. The major prospects and challenges of using AI technology in organic waste treatment, as well as the future directions of AI-based waste management practices, are also discussed. This review also provides exclusive coverage of various types of organic waste, conventional organic waste treatment methods and their limitations, as well as the role of organic waste management in achieving the SDGs.
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