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AI Applied to the Circular Economy: An Approach in the Wastewater Sector

Sustainability 2024 6 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 45 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Vicent Hernández-Chover, Águeda Bellver-Domingo, Lledó Castellet-Viciano, Francesc Hernández-Sancho

Summary

This review explored how artificial intelligence can be applied to circular economy objectives in the wastewater sector, examining AI-driven approaches for optimizing water treatment, resource recovery, and system efficiency. The paper identified opportunities and challenges for integrating machine learning into water utility operations within an ecological transition framework.

Body Systems
Study Type Environmental

Water is one of the most basic and essential resources for life and is also a strategic component for the development of the economies of the different countries of the planet. The water sector in the context of ecological transition and the circular economy has enormous economic potential. However, the water resources present in a territory are, in many cases, very limited, and their availability is increasingly restricted. In this respect, current technologies make it possible to generate a whole range of renewable resources. In the case of wastewater treatment plants, in addition to obtaining clean water in sufficient quantity and quality, it is possible to take advantage of multiple other resources generated in the purification processes, such as fertilizers, biogas, bioplastics, and glass, and even recover adsorbents such as enzymes and proteins from wastewater. These resources represent a valuable social, environmental, and economic contribution. The scarcity of some of these resources causes continuous increases in market prices, generating economic tensions between producers and potential users. This work proposes to guide the potential of artificial intelligence (AI)-based methodologies in aspects related to the supply and demand of the resources generated in these infrastructures. Specifically, the use of machine learning (ML) allows for projecting economic scenarios based on multiple variables, such as the quality and quantity of the treated flows, the resources generated in the infrastructures, the current demands, and the prices of substitute goods. This aspect represents a substantial advance in terms of the circular economy since, beyond the technical aspects related to the processes, it ensures a sustainable balance between potential producers and end users. In conclusion, it brings sustainability to the urban water-cycle sector, ensuring the viability of the resources generated.

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