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A Graph Theory Modelling Approach for the Optimal Operation of Water Distribution Systems under Water Quality Constraints

机译:水质约束下配水系统最优运行的图论建模方法

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Optimal water system operation, including hydraulic and water quality constraints are complex problems to solve due to the nonlinear relationship of head-loss to flow and disinfectant-loss to time. A common approach used today is evolutionary algorithms (EA) such as genetic algorithms or others. For large problems with a large number of decision variables, ether over extended period or having several pumping units, the extended solution time of the EA approach may render the approach not relevant. The proposed method utilized the operation graph optimization (OGO) algorithm proposed previously by the authors, demonstrating high speed discrete minimal cost, optimization with hydraulic constraints. A minimal cost algorithm is proposed, including hydraulic and water quality constraints. The suggested algorithm raises the concentration of the residual chlorine in the network by optimally decreasing the operational volume of the water tanks, and by such increasing the pump switching frequency. The algorithm is demonstrated and compared to enumeration on a single pressure zone example network (1 water tank, 1 pumping unit), on a large example network (C-Town, 7 water tanks, 11 pumping units). The resulting pump schedule is not a global minimum when compared to the best enumeration result on a single pressure zone. However, the algorithm may serve, especially in large water systems, as a quick and feasible answer to system operators, regarding the water volume to maintain in the different tanks to provide minimal chlorine service concentration at near minimal cost.
机译:最佳水系统运行,包括液压和水质限制是复杂的问题,因为由于头部损失的非线性关系,流动和消毒失去时间的非线性关系。今天使用的常见方法是进化算法(EA),例如遗传算法或其他遗传算法。对于大量决策变量的大问题,延长时段或具有多个泵送单元的ether,EA方法的扩展解决时间可能会使方法不相关。所提出的方法利用了先前由作者提出的操作图优化(OGO)算法,展示了高速离散的最小成本,用液压约束优化。提出了一种最小的成本算法,包括液压和水质限制。通过最佳地降低水箱的操作体积,并通过这种增加的泵开关频率来提高网络中残留氯的浓度提高了网络中的残余氯的浓度。对算法进行说明,并与单个压力区示例网络(1个水箱,1个泵送单元)上的枚举进行比较,在一个大的示例网络上(C-Town,7个水箱,11个泵送单元)。与单个压力区上的最佳枚举结果相比,产生的泵计划不是全局最小值。然而,该算法可以作为系统运营商的快速可行答案,特别是在不同罐中维持的水上体积,以在不同的成本下提供最小的氯服务浓度。

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