MachIne learning for nutrient recovery in the smart city circular economy – A review

Soo, A; Wang, L; Wang, C; Shon, HK

MachIne learning for nutrient recovery in the smart city circular economy – A review

Soo, A Wang, L Wang, C Shon, HK

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Process Safety and Environmental Protection, 2023, 173, pp. 529-557
Issue Date:: 2023-05-01

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Field	Value	Language
dc.contributor.author	Soo, A
dc.contributor.author	Wang, L
dc.contributor.author	Wang, C
dc.contributor.author	Shon, HK
dc.date.accessioned	2023-05-06T09:59:39Z
dc.date.available	2023-05-06T09:59:39Z
dc.date.issued	2023-05-01
dc.identifier.citation	Process Safety and Environmental Protection, 2023, 173, pp. 529-557
dc.identifier.issn	0957-5820
dc.identifier.uri	http://hdl.handle.net/10453/170254
dc.description.abstract	Urbanisation is leading to a concentration of growing city populations that contribute significantly to economic growth, while becoming epicentres of waste generation, greenhouse gas emissions, and food consumption. Nutrient smart city circular economy is currently an understudied intersection of growing city populations of food consumers, nutrient recovery technologies, Internet of Things (IoT), and agriculture. Meanwhile, machine learning has exploded with popularity over the years, with many circular economy literatures examining its usefulness in its predictive qualities to support management, optimisation, and recovery of useful resources from organic waste. This review paper examines advancements in machine learning for macronutrient recovery in city organic waste systems for a circular economy. The use of ML will greatly improve the scalability, transparency, productivity and accuracy of nutrient: recovery technologies, logistics, dissemination, and reuse. ML can also be combined with hardware to automate tedious waste separation, recovery and agricultural tasks using drones, hydroponics and satellites. Meanwhile, crop yields, nutrient demand-supply efficiencies, food security, environmental soil monitoring, and prosumer involvement could all increase. However, ML applications for urine, anaerobic digestion and prosumer economics are lacking.
dc.language	en
dc.publisher	Elsevier
dc.relation	http://purl.org/au-research/grants/arc/IH210100001
dc.relation.ispartof	Process Safety and Environmental Protection
dc.relation.isbasedon	10.1016/j.psep.2023.02.065
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0904 Chemical Engineering, 0911 Maritime Engineering, 0914 Resources Engineering and Extractive Metallurgy
dc.subject.classification	Chemical Engineering
dc.subject.classification	Strategic, Defence & Security Studies
dc.title	MachIne learning for nutrient recovery in the smart city circular economy – A review
dc.type	Journal Article
utslib.citation.volume	173
utslib.for	0102 Applied Mathematics
utslib.for	0904 Chemical Engineering
utslib.for	0911 Maritime Engineering
utslib.for	0914 Resources Engineering and Extractive Metallurgy
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/Strength - CTWW - Centre for Technology in Water and Wastewater Treatment
utslib.copyright.status	closed_access	*
dc.date.updated	2023-05-06T09:59:35Z
pubs.publication-status	Accepted
pubs.volume	173

Abstract:

Urbanisation is leading to a concentration of growing city populations that contribute significantly to economic growth, while becoming epicentres of waste generation, greenhouse gas emissions, and food consumption. Nutrient smart city circular economy is currently an understudied intersection of growing city populations of food consumers, nutrient recovery technologies, Internet of Things (IoT), and agriculture. Meanwhile, machine learning has exploded with popularity over the years, with many circular economy literatures examining its usefulness in its predictive qualities to support management, optimisation, and recovery of useful resources from organic waste. This review paper examines advancements in machine learning for macronutrient recovery in city organic waste systems for a circular economy. The use of ML will greatly improve the scalability, transparency, productivity and accuracy of nutrient: recovery technologies, logistics, dissemination, and reuse. ML can also be combined with hardware to automate tedious waste separation, recovery and agricultural tasks using drones, hydroponics and satellites. Meanwhile, crop yields, nutrient demand-supply efficiencies, food security, environmental soil monitoring, and prosumer involvement could all increase. However, ML applications for urine, anaerobic digestion and prosumer economics are lacking.

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http://hdl.handle.net/10453/170254