Wastewater treatment is a critical process in urban and industrial settlements aiming to clean and protect the water as well as the overall environment. Wastewater management systems are conceived explicitly for purifying wastewater, providing clean water efficiently, but this is a hard task due to frequent and quite unpredictable fluctuations of inlet wastewater flows, arising from (random) rain water or (periodical, e.g. day-night) sewage sources, sometimes also leading to failures and outages. To ensure the quality of the clean water out above a threshold and keep the overall system operating, this paper proposes the smart wastewater intelligent management system (SWIMS). It monitors and controls inlet and outlet flows as well as the water quality and parts of the plant as a cyber-physical system (CPS), starting from an Environmental Internet of Things (EIoT) platform. The data generated from the treatment plant is collected in an information system hosted by a server together with an intelligent system that processes this information in a real-time fashion and provides the feedback for optimizing the plant to maintain a good quality of water over time. Such an intelligent system exploits deep learning approaches to control the behaviour of the wastewater treatment system through anomaly detection, supporting decision making on it. SWIMS has been implemented in a real case study deployed in Briatico, Italy. The data and results collected from such a case study are presented, analyzed and discussed in this paper, demonstrating the feasibility and the effectiveness of the SWIMS solution.

SWIMS: The Smart Wastewater Intelligent Management System

Cicceri G.
;
Maisano R.;Morey N.;DIstefano S.
2021

Abstract

Wastewater treatment is a critical process in urban and industrial settlements aiming to clean and protect the water as well as the overall environment. Wastewater management systems are conceived explicitly for purifying wastewater, providing clean water efficiently, but this is a hard task due to frequent and quite unpredictable fluctuations of inlet wastewater flows, arising from (random) rain water or (periodical, e.g. day-night) sewage sources, sometimes also leading to failures and outages. To ensure the quality of the clean water out above a threshold and keep the overall system operating, this paper proposes the smart wastewater intelligent management system (SWIMS). It monitors and controls inlet and outlet flows as well as the water quality and parts of the plant as a cyber-physical system (CPS), starting from an Environmental Internet of Things (EIoT) platform. The data generated from the treatment plant is collected in an information system hosted by a server together with an intelligent system that processes this information in a real-time fashion and provides the feedback for optimizing the plant to maintain a good quality of water over time. Such an intelligent system exploits deep learning approaches to control the behaviour of the wastewater treatment system through anomaly detection, supporting decision making on it. SWIMS has been implemented in a real case study deployed in Briatico, Italy. The data and results collected from such a case study are presented, analyzed and discussed in this paper, demonstrating the feasibility and the effectiveness of the SWIMS solution.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11570/3214700
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