Shankar Narasimhan S

Urban water distribution networks (WDNs) are large scale complex systems with limited instrumentation. Due to aging and poor maintenance, significant loss of water can occur through leaks. We present a method for leak detection in WDNs using repeated water balance and minimal use of additional off-line flow measurements. A multi-stage graph partitioning approach is used to determine where the off-line flow measurements are to be made, with the objective of minimizing the measurement cost. The graph partitioning problem is formulated and solved as a multi-objective mixed integer linear program (MILP). We further derive an approximate method inspired by spectral graph bisection to solve the MILP, which is suitable for very large scale networks. The proposed methods are tested on large scale benchmark networks, and the results indicate that on average, flows in less than 3% of the pipes need to be measured to identify the leaky pipe or joint.

Optimal operation of water distribution networks (WDNs) is concerned with meeting consumer demands at desired pressures in an efficient and equitable manner while conserving resources. This can be achieved by implementing advanced control schemes such as model predictive control (MPC). If sufficient water is available, the control objective is to meet consumer demands while preventing wastage. On the other hand, if the available water is insufficient or inadequate to meet consumer demands at the required pressures, equitable distribution of the available resource is of primary importance. In this contribution, a nonlinear model predictive controller is proposed for optimal operation of WDNs that can deal with both the above situations. The proposed approach takes into account availability of storage facilities at the source and demand points. In addition, the control algorithm can account for plant-model mismatch. Performance of the proposed model based control strategy is illustrated through numerical simulations of an illustrative WDN operating under various water availability scenarios. In the water sufficient scenario, the proposed MPC strategy is able to meet the consumer requirements while minimizing the excess amount of water supplied. In the water deficient scenario, the MPC algorithm is able to exploit the available storage facilities at consumer end to reduce the daily supply deficit by about 20%. Using a longer prediction horizon in MPC results in a further reduction of about 40% in the daily supply deficit.

Water Distribution Networks in many rural areas supply water from storage reservoirs at the source of water to a group of villages through gravity or by pumping. Due to poor operational policies, they often fail to meet the demand of the beneficiary villages. This work proposes a rational technique for scheduling the supply in such networks to maximize the water delivered to the villages in an equitable manner. A secondary objective is to minimize the number of valve operations. The problem is formulated as a LP followed by an ILP. The parameters in the optimization problem are obtained by simulations of a well calibrated hydraulic model or experimental data if such a model is not available.

The nexus between water and energy reveals that transporting water for end use is a highly energy intensive operation. In this work we consider the optimal operation of a water distribution network consisting of pumps delivering water to different reservoirs, with each reservoir catering to a time varying demand. Pumps and ON/OFF valves are used as manipulated variables to minimize energy consumption while meeting the demand. Due to the nonlinear nature of the pump operating curve and the hydraulics, this results in a Mixed Integer Nonlinear Program (MINLP). We propose a three step decomposition approach to solve this problem efficiently. The applicability of this technique is demonstrated on a water network proposed for a municipality in India and the potential advantages are reported. We also compare the solution times required for the proposed technique and a standard solver and demonstrate the efficiency of the proposed approach.

Optimal operation of water distribution networks can be posed as a scheduling problem where the objective is to meet the time varying demand while meeting constraints on supply, pressure etc. In the present work, we propose a robust optimization problem to address uncertainty in the parameters of the model used for optimization. The resulting problem is a second order cone program that can be solved efficiently. The formulation ensures a high probability of meeting the demands, adding to the practical significance. Further, we provide the results of applying this technique on a laboratory scale water distribution network.

With the demand for water increasing rapidly, optimal operation of Water Distribution Networks (WDNs) is necessary to provide consumers with the maximum amount of water possible in an equitable manner. This paper presents the outcomes of an experimental investigation of supply policies implementable on rural WDNs. Tests conducted on a fully automated lab scale network, configured to represent rural WDNs, shows significant variations in supply time with the operational policy followed. Further, a systematic set of experiments are carried out to determine the flow rates in different network configurations and this data is later used to formulate a linear programming problem that identifies an optimal operational policy for the system.