A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Control and Testing on a Smart Water Infrastructures Laboratory
Over the past few decades, Water Resource Management (WRM) has become an extremely complex problem due to rapid urbanization, the new threats of climate change, and the increasing water demand from industry. The concept of Smart Water Infrastructure Management (SWIM) considers water infrastructures where operational management is necessary and can be improved, supported, or even replaced with using high-level control techniques. However, real-time field testing of newly-developed monitoring, control, and fault detection techniques is typically very restrictive in WRM applications due to the high safety requirements. For that reason, a laboratory environment where methods are tested on real water and real flow can significantly improve the support of decision-making and enable the methods to be deployed on real systems. The Smart Water Infrastructures Laboratory (SWIL) at Aalborg University is a modular test facility that can be configured to emulate Water Distribution Networks, Wastewater Collection, and District Heating Systems. The modularity of the laboratory allows performing experiments with different network topologies, multiple hydrological scenarios and to emulate leakages and overflows, which in a real-case scenario would possibly harm the infrastructure and would cause discomfort to end-users. The laboratory focuses on how control technology can provide new solutions to problems in water cycle management. The current research areas include Leakage detection and isolation, safe learning for network management, and overflow prevention in open-channel sewer applications. For water distribution networks, leakage detection and isolation algorithm are developed which utilizes a self-adaptive reduced-order network model to predict nominal pressure in the network. These predicted pressures are used to generate pressure residuals, which are further compared to expected residual signatures to isolate a leakage. Moreover, machine learning methods like Reinforcement Learning can provide an optimal-adaptive policy ...
Control and Testing on a Smart Water Infrastructures Laboratory
Over the past few decades, Water Resource Management (WRM) has become an extremely complex problem due to rapid urbanization, the new threats of climate change, and the increasing water demand from industry. The concept of Smart Water Infrastructure Management (SWIM) considers water infrastructures where operational management is necessary and can be improved, supported, or even replaced with using high-level control techniques. However, real-time field testing of newly-developed monitoring, control, and fault detection techniques is typically very restrictive in WRM applications due to the high safety requirements. For that reason, a laboratory environment where methods are tested on real water and real flow can significantly improve the support of decision-making and enable the methods to be deployed on real systems. The Smart Water Infrastructures Laboratory (SWIL) at Aalborg University is a modular test facility that can be configured to emulate Water Distribution Networks, Wastewater Collection, and District Heating Systems. The modularity of the laboratory allows performing experiments with different network topologies, multiple hydrological scenarios and to emulate leakages and overflows, which in a real-case scenario would possibly harm the infrastructure and would cause discomfort to end-users. The laboratory focuses on how control technology can provide new solutions to problems in water cycle management. The current research areas include Leakage detection and isolation, safe learning for network management, and overflow prevention in open-channel sewer applications. For water distribution networks, leakage detection and isolation algorithm are developed which utilizes a self-adaptive reduced-order network model to predict nominal pressure in the network. These predicted pressures are used to generate pressure residuals, which are further compared to expected residual signatures to isolate a leakage. Moreover, machine learning methods like Reinforcement Learning can provide an optimal-adaptive policy ...
Control and Testing on a Smart Water Infrastructures Laboratory
Balla, Krisztian Mark (author) / Ledesma, Jorge Val (author) / Rathore, Saruch Satishkumar (author) / Wisniewski, Rafal (author) / Kallesøe, Carsten Skovmose (author)
2021-10-21
Balla , K M , Ledesma , J V , Rathore , S S , Wisniewski , R & Kallesøe , C S 2021 , ' Control and Testing on a Smart Water Infrastructures Laboratory ' , 7th Young Water Professionals Denmark Conference , Copenhagen , Denmark , 21/10/2021 - 22/10/2021 pp. 1-2 .
Conference paper
Electronic Resource
English
Smart Water Infrastructures Laboratory , Control , data-driven , safe control , sewer networks , drinking water networks , configurable lab , test laboratory , water cycle management , fault detection , Reinforcement Learning , Model Predictive Control , Adaptive algorithms , System Identification , District Heating , Waste Water , water resource management , /dk/atira/pure/sustainabledevelopmentgoals/clean_water_and_sanitation , name=SDG 6 - Clean Water and Sanitation , /dk/atira/pure/sustainabledevelopmentgoals/climate_action , name=SDG 13 - Climate Action
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