Smart Network Control in Water Distribution Systems
Our work will advance smart network controls for water network to incorporate the identify energy recovery locations and optimise energy performance through the installation of MHP turbines with a dual pressure and energy production function. This work will also consider pump storage within water networks, including sizing and locating these storage systems.
From a control perspective, managing large scale water networks is particularly challenging. The complexity of network optimisation is due to several factors: (i) the high level of interconnectivity in a system, (ii) diverse performance criteria and stringent operational constraints, (iii) social and legislative factors, and (iv) consumer behaviour. Different approaches will be explored, for example model predictive control (MPC) has the ability of minimizing the cost associated with water treatment and pumping. Water demand models are important for forecasting water consumption patterns or trends for the network control system. Therefore, we will develop an innovative multi-scale control algorithm in the MPC framework for efficient management, as well as develop and demonstrate multiscale controllers in the framework of MPC software to enable improved energy and cost performance of water networks in Ireland and Wales.
Our research team in Dublin focus on developing software solutions for the control of water distribution networks using MPC, as an initial step we aimed in small water network in Ireland and developed a control strategy using linear model predictive control to reduce pumping cost. It was concluded that there exists significant cost reduction to the pumping cost by reducing the pump speed while adhering to the demand of the network. This work was presented in 17th International Computing and Control Conference in Exeter, United Kingdom as a poster presentation. The conference was held from 1st of September to the 4th of September.
We will explore hardware solutions for smart control system of water networks. This will help us optimise energy recovery by providing detail of energy consumption and CO2 emissions. By assessing existing network infrastructure, it will allow us to develop new costed control strategies for hardware components to reduce environmental impacts. This will help improve the efficiency of water pump variable speed and variable frequency drives, and control devices. In addition, it will allow for the incorporation of micro-hydropower and distributed pumped storage tanks in the network.