[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124396-en":3,"doc-seo-124396-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124396,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Machine learning framework for wastewater circular economy - Towards smarter nutrient recoveries","As geopolitical instability disrupts mineral and raw-material supply chains and accelerates net-zero ambitions, policies increasingly emphasize circular economies that reduce virgin material use, lower carbon emissions, and slow landfilling. Phosphorus and nutrients depend on finite reserves and are present in municipal wastewater, motivating transformation of WWTPs from linear treatment-for-disposal to nutrient-recovery centers. This framework positions machine learning as an enabler of scalable, cost-effective, safer nutrient and value-product recovery, linked to economic, societal, technical, and commercial factors via open-data connectivity, and provides a policy guide for institutions to advance food, energy, and water security through ML-enabled circular economy WWTPs.","Desalination 592 (2024) 118092  \nContents lists available at ScienceDirect  \nDesalination  \njournal [homepage: www.elsevier.com/locate/desal](homepage: www.elsevier.com/locate/desal)  \n| Machine learning framework for wastewater circular economy   Towards   smarter nutrient recoveries\u003Cbr>Allan Soo a, Li Gaob, Ho Kyong Shona,*\u003Cbr>a School of Civil and Environmental Engineering, University of Technology Sydney (UTS), New South Wales, Australia b South East Water Corporation, 2268, Seaford, VIC 3198, Australia |  |\n| --- | --- |\n| H I G H L I G H T S\u003Cbr>• There is a weak utilisation of ML in CE WWTPs.\u003Cbr>• Critical material regulations inadequately cover phosphorous circularity for WWTP.\u003Cbr>• Further data collections and ML training with WWTP needed for full benefits\u003Cbr>• ML an enabler for cost-effective, safer ML CE WWTP commercialisation\u003Cbr>• ML utilisation must be strengthened to achieve higher nutrient circularity.\u003Cbr>A R T I C L E I N F O\u003Cbr>Keywords:\u003Cbr>Machine learning Circular economy Nutrient\u003Cbr>Sustainable supply chains | G R A P H I C A L A B S T R A C T\u003Cbr>A B S T R A C T\u003Cbr>As the world’s supply chains become disrupted through geopolitical instability and the race towards a net-zero future, policies have been implemented to improve the security of certain minerals and raw materials critical to a country’s survival and sustainability goals. Circular economies (CE) are sought to be an ecosystem that will reduce virgin material consumption rates, lower carbon emissions, and decelerate the rate of landfilling. However, cost-effective and commercially attractive substitutes to conventional products are needed for this to be realised. Machine learning (ML) and the explosion of interest in artificial intelligence (AI) have led to growing interests in predictive and generative applications for sustainability. Phosphorous and, nutrients overall, operate on finite reserves essential for food supply chains; while such nutrients are largely present in municipal wastewater streams. Wastewater treatment plants (WWTPs) must then face a transformational force to become nutrient recovery centres, rather than follow a linear treat-for-disposal model. In this framework paper, ML is positioned as an enabler for scaled, cost-effective and safer recovery of nutrients and other valuable products—tying in economic, societal, technical and commercial factors through open data connectivity. Moreover, the paper issues a policy guide for institutions wishing to advance food, energy and water security through machine learning, circular economy wastewater treatment plants (ML CE WWTP). |\n\n* Corresponding author.  \nE-mail [address:](address: Hokyong.Shon-1@uts.edu.au)[ Hokyong.Shon-1@uts.edu.au](address: Hokyong.Shon-1@uts.edu.au) (H.K. Shon).  \n[https://doi.org/10.1016/j.desal.2024.118092](https://doi.org/10.1016/j.desal.2024.118092)  \nReceived 20 July 2024; Received in revised form 4 September 2024; Accepted 5 September 2024 Available online 7 September 2024  \n0011-9164/© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)).  \nA. Soo et al. Desalination 592 (2024) 118092  \nNomenclature  \nSymbols Variables  \nLFI linear flow index  \nF(x) utility index  \nV mass of virgin nutrient. E.g., 14 g of virgin N per kg  \nswine feed. M mass of finished product  \nW unrecoverable waste  \nFR recycled materials  \nFS sustainable sourced materials  \nFD reused materials  \nW e.g., 5.7 g N wasted/kg swine feed produced from  \nrecovered N.  \nCR mass fraction of the product that is recycled  \nCU mass fraction of the product that is reused  \nCS mass fraction of the product that is composted  \nCE mass fraction of the product that is incinerated  \nL average lifetime of the product  \nLavg average lifetime of the product by industry standards U functional units consumed  \nUavg industry average for this functional consumption  \n1. 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It connects technical recovery processes with economic, societal, and commercial factors through open data connectivity.\"},{\"question\":\"What policy-related guidance does the framework provide?\",\"answer\":\"The paper includes a policy guide for institutions aiming to improve food, energy, and water security by advancing machine-learning-enabled circular economy wastewater treatment plant approaches.\"}]","Machine learning framework for wastewater circular economy - Towards smarter nutrient recoveries | PDF",1785821983,48,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-framework-for-wastewater-circular-economy-towards-smarter-nutrient-recoveries","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-framework-for-wastewater-circular-economy-towards-smarter-nutrient-recoveries/124396/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is nutrient circularity important for wastewater treatment plants?","Question",{"text":75,"@type":76},"Nutrients like phosphorus rely on finite reserves for food supply chains. 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