USING LATIN HYPERCUBE SAMPLING TECHNIQUE FOR MULTIOBJECTIVE OPTIMIZATION OF WATER SUPPLY SYSTEM DESIGN UNDER UNCERTAINTY

Abstract

Water demand uncertainty in a water supply system (WSS) arises mainly due to the abnormal behaviors of water users and the change of network configuration when it is expanded to new consumers. In practice, this directly impacts the optimal designed WSS. This paper presents a methodology to address the issue of water demand uncertainty in the designing a WSS that combines the Latin Hypercube Sampling Technique (LHST) and a multiobjective algorithm optimization. The two objectives are: (1) minimisation of capital cost, and (2) maximization of WSS robustness. The decision variables are the pipe diameter alternatives for each pipe in the network under constraints of nodal head limitations. The output from the multiobjective algorithm optimization process is the Pareto front containing design solutions which are the trade-off solutions in terms of the two objectives. Both cases of uncertain uncorrelated and correlated demand were taken into account. The new methodology is tested on two benchmark published water supply systems: Two loop network and Hanoi network. With only thousand samples, the LHST was capable of producing a good range of random output variables corresponding to uncertain input variables. The result will support more options for designers to select the most appropriate network configuration and it is clear that neglecting demand uncertainty may lead to a seriously under-designed network.