Machine learning framework for wastewater circular economy — Towards smarter nutrient recoveries

Soo, A; Gao, L; Shon, HK

Machine learning framework for wastewater circular economy — Towards smarter nutrient recoveries

Soo, A Gao, L Shon, HK

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Desalination, 2024, 592
Issue Date:: 2024-12-21

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Field	Value	Language
dc.contributor.author	Soo, A
dc.contributor.author	Gao, L
dc.contributor.author	Shon, HK
dc.date.accessioned	2025-04-02T05:09:50Z
dc.date.available	2025-04-02T05:09:50Z
dc.date.issued	2024-12-21
dc.identifier.citation	Desalination, 2024, 592
dc.identifier.issn	0011-9164
dc.identifier.issn	1873-4464
dc.identifier.uri	http://hdl.handle.net/10453/186484
dc.description.abstract	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).
dc.language	English
dc.publisher	ELSEVIER
dc.relation	http://purl.org/au-research/grants/arc/IH210100001
dc.relation	Investment NSW
dc.relation	The Korea Foundation
dc.relation	The Korea Foundation
dc.relation.ispartof	Desalination
dc.relation.isbasedon	10.1016/j.desal.2024.118092
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	03 Chemical Sciences, 09 Engineering
dc.subject.classification	Chemical Engineering
dc.subject.classification	34 Chemical sciences
dc.subject.classification	40 Engineering
dc.title	Machine learning framework for wastewater circular economy — Towards smarter nutrient recoveries
dc.type	Journal Article
utslib.citation.volume	592
utslib.for	03 Chemical Sciences
utslib.for	09 Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Technology in Water and Wastewater (CTWW)
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-04-02T05:09:49Z
pubs.publication-status	Published
pubs.volume	592

Abstract:

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).

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http://hdl.handle.net/10453/186484