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.

Liquefied petroleum products such as propane, butane, LPG and LNG are transported by ships and stored in tanks in storage terminals. These products are conveyed from the jetty by above-ground insulated pipelines to storage terminals that are typically situated 12-20. km inland. Unloading of petroleum products is energy intensive and results in excessive BOG (boil off gas) generation and pressure build up in the storage tank. In this work, a dynamic model of the unloading operation is developed to predict temperature dynamics of pipeline, pressure and inventory changes in the storage tank. This model consists of a lumped dynamic model for the storage tank coupled with a distributed parameter model for the pipeline. The model is validated using limited data collected from an operating plant during unloading of propane. Dynamic simulation results of the model are presented to indicate the trade-off that exists between the pre-cooling and unloading operating strategies. © 2012 Elsevier Ltd.

Petroleum products such as propane, butane, liquified propane gas, and liquified natural gas are unloaded from ship carriers through pipelines to storage facilities located 10-12 kms inland. This process is an energy intensive and a time-consuming operation. Unloading at high flow rates results in excessive boil-off gas (BOG) generation in the storage tank thereby resulting in increased cost of pumping the fluid and increased cost of compressing the resulting BOG. If the BOG generated exceeds the capacity of the compressors, then the excess gas has to be flared resulting in loss of valuable raw material, besides the associated environmental damage. But, when low unloading flow rates are employed, it results in longer berthing time of the ship to unload the product thus incurring demurrage. In this paper, efficient ways to minimize the total cost of an unloading operation are explored by solving a generalized simulation optimization model and the results are discussed. The strategies and the results presented in this work are valuable for industrial applications. © 2014 American Chemical Society.

Small RO based desalination plants driven by solar power are attractive options, especially in areas with unreliable grid power supply. Batteries or other energy storage devices can be used to buffer against the inherent variability in solar power. However, the capital and maintenance cost of batteries and problems associated with their safe disposal renders this option unattractive. The focus of this work is optimal operation of an RO plant driven by solar power without batteries under continuously varying solar radiation intensity. A model of an RO plant driven by solar power without batteries is developed and demonstrated to have adequate predictive capability. The model is used to determine optimal operation policies under two different methods of operation, namely, (i) without storage of permeate and (ii) with permeate storage. Examination of the optimal solution in the first mode of operation reveals that a simple, robust, and model free implementation strategy using active constraint control is possible. Comparison of results show that accounting for the permeate storage results in a higher permeate production, and the buffer tank can be viewed as a surrogate energy storage device.

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.