Computationally Intensive Design of Water Distribution Systems PDF Download

Author: Manuel Alejandro Andrade-Rodriguez
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Languages : en
Pages : 208

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Book Description
The burdensome capital cost of urban water distribution systems demands the use of efficient optimization methods capable of finding a relatively inexpensive design that guarantees a minimum functionality under all conditions of operation. The combinatorial and nonlinear nature of the optimization problem involved accepts no definitive method of solution. Adaptive search methods are well fitted for this type of problem (to which more formal methods cannot be applied), but their computational requirements demand the development and implementation of additional heuristics to find a satisfactory solution. This work seeks to employ adaptive search methods to enhance the search process used to find the optimal design of any water distribution system. A first study presented here introduces post-optimization heuristics that analyze the best design obtained by a genetic algorithm--arguably the most popular adaptive search method--and perform an ordered local search to maximize further cost savings. When used to analyze the best design found by a genetic algorithm, the proposed post-optimization heuristics method successfully achieved additional cost savings that the genetic algorithm failed to detect after an exhaustive search. The second study herein explores various ways to improve artificial neural networks employed as fast estimators of computationally intensive constraints. The study presents a new methodology for generating any large set of water supply networks to be used for the training of artificial neural networks. This dataset incorporates several distribution networks in the vicinity of the search space in which the genetic algorithm is expected to focus its search. The incorporation of these networks improved the accuracy of artificial neural networks trained with such a dataset. These neural networks consistently showed a lower margin of error than their counterparts trained with conventional training datasets populated by randomly generated distribution networks.