Search

Water distribution network that includes supply reservoirs, overhead tanks, consumer demand nodes, interconnecting pipes, lifting pumps, and control valves is the main mode of water supply for majority of the communities especially in urban areas. Supply of required quantity of water and at right time is the primary objective of water distribution network analysis. The analysis of water distribution networks can be broadly classified into design and operation problems and both problems have been the focus of many researchers over the past three decades. In the water distribution network design problems, the target is attaining the cost effective configuration that satisfies the minimum hydraulic head requirement at the demand nodes. In this paper, a new algorithm for design of water distribution network namely “heuristics-based algorithm” which completely utilizes the implicit information associated with the water distribution network to be designed has been proposed and validated with two water distribution networks. It is found that the proposed algorithm performs well for the least-cost design of water distribution networks. [ABSTRACT FROM AUTHOR]

The efficient and effective search for the optimum design solution of a water distribution network using genetic algorithms (GAs) is governed by several factors such as representation scheme, population size, hydraulic simulation model, fitness function, penalty method, GA operators, number of generations, and more importantly the size of the search space. This paper proposes a modified GA that uses basic operators along with their derivatives randomly. Further, a methodology based on critical path method is suggested to reduce the search space. A software tool, GA-WAT, based on the proposed methodology is developed and first tested and verified for its efficiency and effectiveness on two previously published single source networks. Later, it is applied to the optimal design of a larger, two-source hypothetical network. The results obtained indicate that the modified GA with reduction in search space proposed herein is more effective, especially for large practical networks. [ABSTRACT FROM AUTHOR]

This paper develops a multicommodity flow model to optimize water distribution and water quality in a regional water supply system. Waters from different sources with different qualities are considered as distinct commodities that concurrently share a single water distribution system. The model can accommodate two-way flow pipes, represented by undirected arcs, and the perfect mixing condition. Additionally, blending requirements are specified at certain control nodes within the system to ensure that downstream users receive the desired water quality. The optimization model is highly nonlinear and solved by a hybrid genetic algorithm (GA). The GA is first used to globally search for the directions of all undirected arcs. Then a generalized reduced gradient (GRG) algorithm embedded in the GA is used to optimize the objective function for fitness evaluation. The proposed methodology was first tested and verified on a hypothetical system and then applied to the regional water distribution system of the Metropolitan Water District of Southern California. The results obtained indicate that the proposed hybrid GA is a viable way of converting an undirected network to a directed network by separating the complicating variables, and that the resulting directed network model can be solved iteratively and efficiently by a gradient-based algorithm. [ABSTRACT FROM AUTHOR]

A method for near-optimal real-time on-line operation of urban water distribution system is presented and demonstrated. It uses a reduced model (RM) of the network which reproduces its performance over time with high fidelity with optimization by a genetic algorithm (GA). The RM-GA software uses network data, forecasted demands for an operational planning time-horizon (24 h ahead), field data from a SCADA on the current status of the network, time-of-day energy cost data, and operator-imposed constraints on tank water levels and demand junctions pressures. The GA looks for the near-least-cost operation plan for the whole time horizon, using the reduced network model, and activates the first hour of the plan through the SCADA. At the end of the hour, the status of the network is updated from field data, the time horizon is rolled ahead by 1 h, and the process repeated. The software was developed for and demonstrated on the distribution system of Haifa. Experiments were conducted, in which the operation which simulates the operators' set-point operation is compared with that obtained by the RM-GA, and energy cost savings of 8 and 10% were calculated for a summer and winter month, respectively. [ABSTRACT FROM AUTHOR]

Surge vessels provide efficient protection against low and high transient pressures in water distribution systems. However, they can be quite expensive, and any reduction in surge vessel size can significantly reduce surge protection costs. Graphical and other heuristic methods reported in literature are limited to sizing surge vessels for simple rising mains. Attempts to use more structured optimization techniques have been largely unsuccessful because of their impractical computational requirements. This article proposes a robust framework for developing surge protection design tools and demonstrates the usefulness of the framework through an example surge vessel sizing tool. The essence of the proposed framework is in the identification of key transient response parameters that influence surge vessel characteristics from seemingly unmanageable transient response data. This parameterization helps the sizing tool to exploit the similarity between transient responses of small pipe networks and subsections of large pipe networks. The framework employs a knowledge-base of transient pressures and flows derived from several small network models and corresponding optimal surge vessel sizes obtained from genetic algorithm (GA) optimizers. Key transient response parameters were identified from this knowledge-base and used as input variables for a neural network model along with the associated surge vessel characteristics as output variables. The trained neural network model was successfully applied for complex pipe networks to obtain optimal surge vessel sizes for transient protection. Neural network model predictions were compared with optimal surge vessel characteristics, and their performance was evaluated by transient simulation models to assess the efficiency of the proposed framework and sizing tool. Application of this framework has the advantage of developing surge protection sizing tools independent of network system schematics and boundary conditions. [ABSTRACT FROM AUTHOR]

This study introduces a new search method for box-constrained optimization problems called the search method for box optimization (SMBO). SMBO is a population heuristic-based search methodology that solves global optimization problems. SMBO represents the population as a probability density function (PDF) inside the problem bounds. The PDF shape is dynamically adapted during the process to guide to a 'good' search domain. The applicability and the efficiency of the method are demonstrated using two benchmark sets, which include unimodal, multimodal, expanded, and hybrid composition functions. The performance of SMBO is compared with several genetic algorithms (GAs); the first benchmark compares it with nine codes of traditional/classic GAs, and the second compares SMBO with two recent variants of genetic algorithms. The results show that SMBO performs as well as or better than the GAs in both comparisons. The method is demonstrated on a nonlinear model for management of a water supply system (WSS), and the results are compared with the commercial GA toolbox of matrix laboratory (MATLAB). [ABSTRACT FROM AUTHOR]

The goal of calibrating a water distribution model is to establish a numerical model that represents the real system. Given the uncertainty in field data, recent studies have examined model parameter and predictive uncertainties and how those uncertainties can guide future data collection experiments. However, a model is to be used in some decision making process and those decisions can be influenced by the model uncertainty. Therefore, a methodology is presented here to evaluate the impact of uncertainty in pipe roughness values on decisions that are made using the model for a system expansion design using a steady state hydraulic model. To complete the analysis, model parameter uncertainty is evaluated using a first order second moment (FOSM) analysis of uncertainty. Parameter uncertainties are then propagated to model prediction uncertainties through a second FOSM for a defined set of demand conditions. Finally, model prediction uncertainties are embedded in an optimal design model to assess the effect of the uncertainties on model-based decisions. If uncertainty levels are large, the monetary benefits of reducing uncertainties from additional data collection can be addressed directly by examining the change in the design cost with additional data. For demonstration, the methodology is applied to a small literature network. Results suggest that the cost reductions are related to the convergence of the mean parameter estimates and the reduction of parameter variances. The impact of each factor changes during the calibration process as the parameters become more precise and the design is modified. Identification of the cause of cost changes, however, is not always obvious. [ABSTRACT FROM AUTHOR]

Cost of effective design or rehabilitation of the pipe networks depends largely on the available budget and satisfying pressures expected at the demand nodes. With the available market pipe sizes, one or more solutions of the same cost, the least cost, and simultaneously meeting the pressure constraints can be obtained from EPANET, a water distribution network simulation model, coupled with an optimization algorithm. Often, however, the least cost may be prohibitive from the overall budget consideration especially in developing countries and a less optimal solution is therefore the only option. The study shows a scheme in which solution selection is made, among solutions, which (1) is within allowable budget; and (2) yields acceptable total pressure deficit distributed more equally at several nodes instead of loaded on one or a few nodes only. A multiobjective optimization algorithm (NSGA-II) is coupled with water distribution network simulation software (EPANET) to provide the much needed Pareto front of the cost and nodal pressure deficit. A two-looped simple network is used to demonstrate the application of the scheme. [ABSTRACT FROM AUTHOR]

A new approach for reliability-based optimization of water distribution networks is presented. The approach links a genetic algorithm (GA) as the optimization tool with the first-order reliability method (FORM) for estimating network capacity reliability. Network capacity reliability in this case study refers to the probability of meeting minimum allowable pressure constraints across the network under uncertain nodal demands and uncertain pipe roughness conditions. The critical node capacity reliability approximation for network capacity reliability is closely examined and new methods for estimating the critical nodal and overall network capacity reliability using FORM are presented. FORM approximates Monte Carlo simulation reliabilities accurately and efficiently. In addition, FORM can be used to automatically determine the critical node location and corresponding capacity reliability. Network capacity reliability approximations using FORM are improved by considering two failure modes. This research demonstrates the novel combination of a GA with FORM as an effective approach for reliability-based optimization of water distribution networks. Correlations between random variables are shown to significantly increase optimal network costs. [ABSTRACT FROM AUTHOR]