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Title	Machine Learning For Nutrient Recovery In The Smart City Circular Economy – A Review
ID_Doc	35968
Authors	Soo A.; Wang L.; Wang C.; Shon H.K.
Year	2023
Published	Process Safety and Environmental Protection, 173
DOI	http://dx.doi.org/10.1016/j.psep.2023.02.065
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. © 2023 The Institution of Chemical Engineers
Author Keywords	Circular economy; Internet of things; Machine learning; Nutrient; Smart city; Sustainability

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