A5050288763- Water Systems and Optimization
- Membrane Separation Technologies
- Groundwater flow and contamination studies
- Water Treatment and Disinfection
- Underwater Vehicles and Communication Systems
- Water Quality Monitoring Technologies
- Energy Load and Power Forecasting
- Advanced Control Systems Optimization
- Hydrology and Watershed Management Studies
- Wastewater Treatment and Nitrogen Removal
- Hydrological Forecasting Using AI
- Probabilistic and Robust Engineering Design
- Water resources management and optimization
- IoT-based Smart Home Systems
- Urban Stormwater Management Solutions
Imperial College London
2023-2024
The provision of self-cleaning velocities has been shown to reduce the risk discolouration in water distribution networks (WDNs). Despite these findings, control implementations continue be focused primarily on pressure and leakage management. This paper considers diurnal flow maximize capacity (SCC) WDNs. We formulate a new optimal design-for-control problem where locations operational settings automatic flushing valves are jointly optimized. formulation includes nonconvex objective...
This paper investigates the problem of integrating optimal pressure management and self-cleaning controls in dynamically adaptive water distribution networks. We review existing single-objective valve placement control problems for minimizing average zone (AZP) maximizing capacity (SCC). Since AZP SCC are conflicting objectives, we formulate a bi-objective design-for-control where locations operational settings automatic flushing valves jointly optimized. approximate Pareto fronts using...
Abstract The formation of discrete regions in a water distribution system, referred to as district metered areas (DMAs), can be pragmatic approach diagnose both system and leakage characteristics. Their application, however, has historically been limited the North American context owing part their costly implementation operational challenges. Both overcome these barriers demonstrate benefits DMAs, testing programme was undertaken Ontario, Canada. Novelty arises from development deployment...
This paper presents an optimal scheduling problem for coordinating pressure and self-cleaning operations in dynamically adaptive water networks. Our imposes a set of time-coupling constraints to manage variations during the transition between operational modes. Solving this time-coupled, nonlinear optimization poses challenges off-the-shelf solvers due its high memory demands. We compare performance decomposition method using alternating direction multipliers (ADMM) with gradient-based...
ABSTRACT Water distribution networks with dynamically adaptive connectivity offer greater operational flexibility. While this strategy has demonstrated improvements in pressure management and network resiliency, further research is needed to better understand its impact on water quality dynamics. This paper investigates the short-term variability of disinfectant residuals a real-world operated dynamic connectivity. We simulate dynamics under two control configurations automatic flushing...
In this paper, we present a new control problem for optimizing pressure and water quality operations in distribution networks. Our imposes set of time-coupling constraints to manage temporal variations, which are exacerbated by the transition between controls. The resulting optimization is nonconvex, nonlinear program with nonseparable structure across time steps. This proves challenging state-of-the-art solvers, often precluding their direct use near real-time large-scale To overcome...
The provision of self-cleaning velocities has been shown to reduce the risk discolouration in water distribution networks (WDNs). Despite these findings, control implementations continue be focused primarily on pressure and leakage management. This paper considers diurnal flow maximize capacity (SCC) WDNs. We formulate a new optimal design-for-control problem where locations operational settings automatic flushing valves are jointly optimized. formulation includes nonconvex objective...